Slope Stability Analysis Using GIS and Numerical Modeling ...

UNIVERSITY GHENT 

UNIVERSITEIT 

GENT 

INTERUNIVERSITY PROGRAMME 

MASTER OF SCIENCE IN 

PHYSICAL LAND RESOURCES 

Universiteit Gent 

Vrije Universiteit Brussel 

Belgium 

Slope Stability Analysis Using GIS and 

Numerical Modeling Techniques 

September 2009 

Promotor: 

Prof. F. De Smedt 

Co-promotor: 

Dr. J. Moeyersons 

Master dissertation in partial fulfilment 

of the requirements for the Degree of 

Master of Physical Land Resources 

by: Shimelies Ahmed Aboye

Slope Stability Analysis Using GIS and 

Numerical Modeling Techniques 

September 2009 

Vrije Universiteit Brussel

i 

Acknowledgments 

First of all, I would like to express my deep and sincere gratitude to my promoter Prof. dr. ir 

F. De Smedt. His critical comments, advice, and guidance have been invaluable to me during 

this research. Above all, I am highly grateful for his timely, detailed and untiring corrections. 

I would like to extend my heartfelt acknowledgment to my co-promoter Dr Jan Moeyersons. 

This study would have never been possible, if it were not for his support in various ways. He 

not only provided me substantial data to work in his lab but also guided me during the 

subsequent data analysis. I am greatly indebted for his patience, understanding and 

encouragement. 

My acknowledgments to VLIR for financially supporting my study. 

Special thanks to Dr ir Philippe Trefois for providing me important technical advice 

throughout this work. I cordially acknowledge Ine Vandecasteele for her support and creating 

a conducive atmosphere during my stay in the Royal Central African Museum. 

My appreciation also goes to Anja Cosemans for her valuable support during my study. I am 

thankful to my friend Eskedil Abebaw for being there when I was in need. I also appreciate 

the cooperation from CORE consulting engineers. 

Finally, I would like to thank my mother Birukt Assaye. I am where I am because of her. 

Shimelies Ahmed 

Brussels, August 2009 

Slope Stability Analysis Using GIS and Numerical Modeling Techniques

ii 

Abstract 

Rain-fall triggered slope instabilities are common problems in the hilly and mountainous 

terrains of the highlands of Ethiopia. To cope up with landslide disasters on a regional basis, 

regional landslide study is considered to be the primary mitigative measure. This study is 

conducted: (1) to determine the landslide susceptibility of a 497 km² area in Hagere Selam, 

northern Ethiopia; (2) to test different numerical models of slope stability analysis, and select 

the most suitable prediction technique that can be used in similar regions; (3) to describe the 

statistical relations of landslide frequency with the physical parameters contributing to the 

initiation of landslides. 

A landslide inventory of the study area is primary compiled from existing digital maps, 

previous studies and aerial photographs. 30 debris flows, 7 landslide belts, 4 landslides in the 

Antalo supersequence, 12 landslides in Imba Degua and Chini ridges, and 2 slumps are 

identified. The landslide database amounts to about 20% of the study area. 

Three steady state scenarios, completely dry, half saturated and saturated, are considered for 

the deterministic modeling. In addition, 4 statistical techniques are treated: statistical index, 

weighting factor, certainty factor and landslide susceptibility methods. For each of these 

approaches, three different combinations of causative factors are employed. A comprehensive 

evaluation of the obtained landslide susceptibility maps is achieved by a model validation 

whereby the outputs are justified by comparing them with the landslide inventory. 

The hazard maps from the statistical index, weighting factor and landslide susceptibility 

methods, are highly similar, as indicated by an average of 75% of the area being classified in 

the same susceptibility class by these models. The Certainty factor and weighting factor 

methods confirm to be more powerful prediction techniques than the other statistical models. 

The half saturated physically based model provides a comparable result to some of the 

statistical outputs. It correctly designates 75% of the inventoried landslides. 

Among the entire hazard maps, certainty factor model 1 is selected to be the best model for 

identifying landslides. The prediction accuracy of this model is 87%. Compared to the other 

methods, it gives the highest posterior probability of landslides in the very high susceptibility 

category (0.107). Moreover, it has the smallest probability of landslide bodies in the low 


iii 

hazard zone (0.005). Accordingly, it is concluded that 41% of the area is under high and very 

high landslide hazard, 15% is moderately susceptible, and 44% is free from landslide risk. 

Based on the combined steady state deterministic model, it is concluded that 34.6% of the 

study area is unconditionally stable, and will not fail under any circumstances unless major 

destabilizing activities occur. If extreme rainfall event leads to full soil saturation, 12.4% of 

the area will face instability under moderate destabilizing actions, 13.7% of the area will 

likely fail under minor destabilizing forces, and 17.7% of the area needs stabilization methods 

to control instability. Landslides will be mobilized in about 16.7% of the area, if the soil is 

half saturated; 4.93% of the area will likely fail under any condition, and hence stabilization 

techniques are recommended. 

Finally, it is indicated that slope gradient, lithology, elevation, slope aspect, and land use are 

statistically significant in predicting slope instability, while faulting, slope shape, proximity to 

drainage lines, and closeness to roads are found to be unrelated to the landsliding in the study 

area. The results of this study demonstrate that slope instability can be effectively modeled by 

using GIS technology and numerical modeling techniques. 


Slope Stability Analysis Using GIS and Numerical Modeling Techniques 

iv

v 

Table of contents 

Acknowledgments .................................................................................................................. i 

Abstract ................................................................................................................................. ii 

Table of contents ....................................................................................................................v 

List of figures ..................................................................................................................... viii 

List of Tables .........................................................................................................................x 

List of symbols .................................................................................................................... xii 

Chapter 1: Introduction ...........................................................................................................1 

1.1 Objectives of the study ..................................................................................................2 

1.2 Study Area ....................................................................................................................2 

1.3 Outline of the thesis ......................................................................................................3 

Chapter 2: Literature review ...................................................................................................5 

2.1 Factors influencing slope stability .................................................................................5 

2.1.1 Gravity ...................................................................................................................5 

2.1.2 The effect of water ..................................................................................................6 

2.1.3 Geological factors ...................................................................................................6 

2.1.4 Soil Properties ........................................................................................................7 

2.1.5 The effect of vegetation ..........................................................................................8 

2.1.6 Triggering events ....................................................................................................8 

2.2 Landslide inventory .......................................................................................................9 

2.3 Anticipation of landslide hazards ................................................................................. 10 

2.4 GIS methods ............................................................................................................... 11 

Chapter 3: Methodology and data preparation ....................................................................... 13 

3.1 Landslide Inventory mapping ...................................................................................... 13 

3.1.1 Mechanism of failures........................................................................................... 17 


vi 

3.2 Geotechnical parameters ............................................................................................. 20 

3.3 Soil depth .................................................................................................................... 25 

3.4 Causative factor maps preparation ............................................................................... 27 

3.4.1 Slope factor ......................................................................................................... 27 

3.4.2 Geological factor ................................................................................................ 28 

3.4.3 Distance to faults factor ....................................................................................... 29 

3.4.4 Elevation factor ................................................................................................... 30 

3.4.5 Land use factor .................................................................................................... 31 

3.4.6 Slope Shape .......................................................................................................... 33 

3.4.7 Aspect .................................................................................................................. 33 

3.4.8 Distance to streams ............................................................................................... 34 

3.4.9 Distance to roads .................................................................................................. 34 

3.4.10 Wetness index ..................................................................................................... 36 

Chapter 4: Deterministic models ........................................................................................... 37 

4.1 Physical based models ................................................................................................. 37 

4.1.1 Stability of infinite slopes ..................................................................................... 37 

4.1.2 Input parameters ................................................................................................... 40 

4.1.3 Critical depth ........................................................................................................ 43 

4.1.4 Factor of safety calculation ................................................................................... 46 

Chapter 5: Statistical Analysis approach ............................................................................... 60 

5.1 General ....................................................................................................................... 60 

5.2 Bivariate statistical analysis ......................................................................................... 61 

5.2.1 Statistical index method ........................................................................................ 61 

5.2.2 Statistical weighting factor analysis ...................................................................... 75 

5.2.3 Certainty factor analysis........................................................................................ 83 

5.2.4 Landslide susceptibility analysis ........................................................................... 91 


vii 

Chapter 6: Comparison and Conclusion ................................................................................ 96 

6.1 Comparison and discussion ......................................................................................... 96 

6.2 Conclusions............................................................................................................... 106 

6.3 Recommendations ..................................................................................................... 112 

References .......................................................................................................................... 113 

Appendix ............................................................................................................................ 123 


viii 

List of figures 

Figure 1.1 Location map of the study area ..............................................................................3 

Figure 3.1 Landslide Inventory map ..................................................................................... 15 

Figure 3.2 Photographic views of some slides ....................................................................... 16 

Figure 3.3 Landslide mechanism 1........................................................................................ 17 

Figure 3. 4 Landslide mechanism 2 ....................................................................................... 18 

Figure 3.5 Areal distribution of geologic layers .................................................................... 20 

Figure 3.6 Slope map ............................................................................................................ 27 

Figure 3.7 Geologic map ...................................................................................................... 29 

Figure 3.8 Distance to faults map .......................................................................................... 30 

Figure 3.9 Elevation map ...................................................................................................... 31 

Figure 3.10 Land use factor map ........................................................................................... 32 

Figure 3.11 Slope shape and Aspect...................................................................................... 33 

Figure 3.12 Distance to streams ............................................................................................ 35 

Figure 3.13 Distance to roads ............................................................................................... 35 

Figure 3.14 Wetness index map ............................................................................................ 36 

Figure 4.1 (a) and (b) - infinite slope force diagrams............................................................. 37 

Figure 4.2 (a) saturated unit weight map, (b) dry unit weight map......................................... 41 

Figure 4.3 (a) cohesion map, (b) angle of internal friction map ............................................. 42 

Figure 4. 4 Critical depth to lower boundary under fully saturated condition ......................... 44 

Figure 4.5 Critical depth, (a) under half saturated, (b) under dry conditions .......................... 45 

Figure 4.6 Areal distribution of critical depths under saturated, half sat and dry scenarios .... 46 

Figure 4.7 Classified factor of safety map under dry condition .............................................. 47 

Figure 4.8 Classified factor of safety map for half saturated condition .................................. 50 

Figure 4.9 Classified factor of safety map under full saturated condition............................... 53 

Figure 4.10 Stability map based on three steady state scenarios ............................................ 56 

Figure 4.11 Areal distribution of stability classes under three steady state scenarios ............. 56 

Figure 4.12 Stability under three steady state scenarios vs slope classes ................................ 58 

Figure 5.1 Percentage of observed training landslides versus LSI (SI model 1) ..................... 67 

Figure 5.2 Landslide hazard map (SI model 1) ...................................................................... 68 

Figure 5.3 Areal distribution of susceptibility classes (SI model 1) ....................................... 68 

Figure 5.4 Percentage of observed training landslides versus LSI (SI model 2) ..................... 70 


ix 



Figure 5.7 % of observed training landslides VS LSI (SI model3)......................................... 73 



Figure 5.10 Percentage of observed training landslides versus LSI. Model 1 (A), Model 2 (B), 

and Model 3 (C) ................................................................................................. 77 

Figure 5.11 Areal distribution of susceptibility classes .......................................................... 78 

Figure 5.12 Landslide hazard map (a) SWF model 1, (b) SWF model 2 ................................ 80 

Figure 5.13 Landslide hazard map (a) SWF model 3 ............................................................. 81 

Figure 5.14 Percentage of observed training landslides versus CF value: Model 1(A), Model 

2(B), Model 3(c), and percentage area of susc. Classes (D)................................. 88 

Figure 5.15 Landslide hazard map (a) CF model 1, (b) CF model2 ....................................... 89 

Figure 5.16 Landslide hazard map CF model 3 ..................................................................... 90 

Figure 5.17 Landslide hazard map (a) LS (model 1), (b) LS (model2)................................... 93 

Figure 5.18 Landslide hazard map LS (model 3) ................................................................... 94 

Figure 5.19 Areal distribution of the hazard classes, LS method ........................................... 94 

Figure 6.1 Comparison of instability classes with the inventory (Mx = model x).................. 97 

Figure 6.2 Comparison between the accuracies of the employed methodologies ................... 98 

Figure 6.3 Cumulative % of observed landslides versus cumulative % area ........................ 102 

Figure 6.4 A map displaying matching between statistical and deterministic methods ......... 106 

Figure 6.5 Final Hazard maps based on (A) CF model 1, and (B) combined steady state 

deterministic methods ......................................................................................................... 111 


x 

List of Tables 

Table 2.1 Triggering events influencing slope stability ...........................................................8 

Table 2.2 Characteristics of landslide susceptibility methods ................................................ 12 

Table 3.1 Shear strength parameters ..................................................................................... 22 

Table 3.2 Range of shear strength parameters for weathered formations ............................... 22 

Table 3.3 Friction angle values for rocks at joints ................................................................. 23 

Table 3.4 Angle of friction and unit weight values of soils derived from geologic layers ...... 23 

Table 3.5 Cohesion and friction angle values of soils derived from geologic layers............... 24 

Table 3.6 Cohesion and friction angle values of soils ............................................................ 24 

Table 3.7 Final compiled shear strength parameters .............................................................. 25 

Table 4.1 Classification of factor of safety values ................................................................. 40 

Table 4.2 Model validation under dry condition .................................................................... 47 

Table 4.3 Stability vs slope classes ....................................................................................... 48 

Table 4.4 Stability vs geologic formations ............................................................................ 48 

Table 4.5 Model validation under half saturated condition .................................................... 50 

Table 4.6 Stability vs slope classes under semi saturated condition ....................................... 51 

Table 4.7 Stability vs geologic formations under semi saturated scenario.............................. 51 

Table 4.8 Model validation under fully saturated condition ................................................... 53 

Table 4.9 Stability versus slope classes ................................................................................. 54 

Table 4.10 Stability vs geologic formations .......................................................................... 54 

Table 4.11 Combined factor of safety classification .............................................................. 55 

Table 4.12 Validation of the model (a) with the whole database (b) with validation group .... 57 

Table 4.13 Stability vs geologic formations .......................................................................... 59 

Table 5.1 Weight assignment, statistical index method ......................................................... 63 

Table 5.2 Validation of SI model 1 ....................................................................................... 69 

Table 5.3 Validation of SI model 2 ....................................................................................... 72 

Table 5.4 Validation SI model 3 ........................................................................................... 74 

Table 5.5 Weighting factor values ........................................................................................ 76 

Table 5.6 Parameters of LSI map and cutoff values .............................................................. 78 

Table 5.7 Validation of SWF models .................................................................................... 81 

Table 5.8 Certainty factor values .......................................................................................... 84 

Table 5.9 Parameters of the CF map and cutoff values .......................................................... 87 


xi 

Table 5.10 Validation of certainty factor method of analysis ................................................. 90 

Table 5.11 Weight values for the landslide susceptibility method ......................................... 92 

Table 5.12 Validation results for landslide susceptibility method of analysis ........................ 95 

Table 6.1 Areal distribution summary (M x = model x) ........................................................ 96 

Table 6.2 Posterior probabilities of all methods .................................................................. 100 

Table 6.3 Agreed areas on stability classes using statistical index method ........................... 102 

Table 6.4 Agreed areas on stability classes using statistical WF method ............................. 103 

Table 6.5 Agreed areas on stability classes using statistical CF method .............................. 104 

Table 6.6 Agreed areas on stability classes by four bivariate methods ................................. 104 

Table 6.7 Agreed areas on by physically based and certainty factor methods ...................... 105 


xii 

List of symbols 

GIS 

Geographic Information Systems. 

Fs 

Factor of safety 

SRTM 

Shuttle Radar Topography Mission 

SI 

Statistical index 

SWF 

Statistical weighting factor 

WF 

Weighting factor 

CF 

Certainty Factor. 

SI_M1 Statistical index model 1 



WF_1 Weighting factor model 1 



CF_1 


CF_2 


CF_3 


LS_1 Landslide susceptibility model 1 



LSS 

Landslide susceptibility 

FAO 

Food and Agricultural organization 

A 

Total area of the entire map 

A* Total area of landslides in the entire map. 

Aij Area of landslides in a certain class i of parameter j. 

CFij Certainty factor given to a certain class i of parameter j. 

DEM 

Digital Elevation Model. 

f 

The landslide density within the entire map. 

Vs 

Versus 

a.m.s.l 

Above sea level 

npix 

Number of pixels 


Slope Stability Analysis Using GIS and Numerical Modeling Techniques 

xiii

Chapter 1: Introduction 

Natural hazards are inherent in mountain environments. Of these, landslides probably 

constitute the most common and dreaded hazard for mountain society everywhere. A 

landslide is defined as the movement of a mass of rock, earth, or debris down a slope. Bates 

& Jackson (1987) defined a landslide as the downslope transport under gravitational influence 

of soil and rock material. Usually the displaced material moves over relatively confined zone 

or surface of shear. 

Destructions caused by catastrophic landslides are a worldwide phenomenon, and the rapid 

increase of population during this century has augmented the problem. Varnes (1981) reported 

that, during the period from 1971-1974 an average of nearly 600 people per year, worldwide, 

were killed by landslides. 

Rainfall triggered landslides are common problems in many areas of the hilly and 

mountainous regions of the highlands of Ethiopia. Gezahegn (1998), Ayalew (1999), 

Temesgen et al. (2001), Nyssen et al. (2002), Ayalew & Yamagishi (2004), Ayenew & 

Barbieri (2004), Woldearegay et al. (2004) indicate landslides in these terrains have been 

affecting human lives, infrastructures, agricultural lands and the natural environment. 

Although landslides have been observed for at least the last three decades, in more recent 

times the sensitivity of both the public and administrative bodies to these events has increased 

as more and more people are now living in the areas affected. Indeed, landslides or landslidegenerated 

problems have claimed about 300 lives, damaged over 100 km of asphalt road, 

demolished more than 200 dwelling houses and devastated in excess of 500 ha of land in 

Ethiopia in the years 1991-1998 (Ayalew, 1999). Despite this, however, the causes and 

mechanisms of slope failures remain poorly understood and so far, little effort has been made 

to reduce losses from landslides and landslide-generated hazards. 

Although it could be said that the occurrence or non-occurrence of landslides depend mainly 

on natural causes and factors, such as geological and geo-hydrological characteristics of any 

locality, exogenous factors such as intense precipitation over a short time interval and 

earthquakes also trigger and accelerate landslides. Similarly, growing human and animal 

populations in mountainous regions create other imbalances such as deforestation and 

encroachment on steep slopes. Construction activities have also a positive contribution to 

slope instabilities. Hence the complexity of these causes increases due to natural and human 


Chapter 1: Introduction 2 

factors. And along with these, the human and economic consequences of such incidents are 

becoming more damaging, adding to the miseries of people who are already in the grip of 

poverty. 

1.1 Objectives of the study 

Most landslide studies in Ethiopia are confined to either individual cases or the hazard prone 

sectors of linear infrastructures. In this paper, a systematic study of landslides, including 

hazard mapping and risk assessment on a larger scale is intended to be carried out. The main 

focus is to assess the stability of slopes from geomorphological, geological and geotechnical 

point of views. Therefore, we will deal with: 

‣ Compiling a landslide inventory for the study area 

‣ Factor of safety calculations and hazard assessment using infinite slope model 

‣ Landslide hazard mapping using four statistical approaches i.e. statistical index, 

statistical weighting factor, certainty factor and landslide susceptibility analysis. In 

addition, three different causative factor combinations will be dealt for each of these 

methods. 

‣ A comparison between the results of the employed methodologies and the inventory. 

‣ A comparison between the accuracies of the models. 

‣ Selection of a suitable physically based scenario and statistical model. 

‣ Matching between statistical model outputs. 

‣ Matching between the selected deterministic and statistical outputs. 

‣ Selection of the final representative hazard map. 

‣ Identifying the responsible triggering factors. 

1.2 Study Area 

The study area of this research is located in Dogua’s Tembien district, Tigray, Ethiopia. It 

encompasses about 497 km² area within the geographic boundaries: 13°34’20’’N to 

13°44’44’’N latitude and 39°05’29’’E to 39°18’58’’E longitude. The altitude ranges between 

1370 and 2835 m a.m.s.l. The region has an average annual rainfall of 750 mm showing a 

bimodal distribution with a first minor peak from March to May, and a second major peak 

(i.e. 80% of the annual rainfall) from June to September. Daily air temperature is explained by 

large variations, from 5°C to 28°C (Nyssen et al., 2005). 



This area, located in the Geba and Werei river catchments, was selected because the highest 

points reveal the most complete geological section of the region, and because of the 

availability of data as several studies have been carried out there (Nyssen et al., 2000; 

Gebremichael et al., 2005; Descheemaeker et al., 2006; Moeyersons et al., 2008; Van Den 

Eeckhaut et al., 2009). Figure 1.1 shows the location of the study area. 

Figure 1.1 Location map of the study area 

1.3 Outline of the thesis 

Chapter 2 presents a brief review of literatures based on factors triggering 

landslides, landslide inventory, Anticipation of landslides and GIS methods. 

In chapter 3, methodology and data preparation will be discussed. Available data 

will be used to generate causative factor maps. Moreover, a landslide inventory 

will be compiled. 

Chapter 4 focuses on deterministic approach of analyzing slopes. Critical 

engineering depth and factor of safety maps will be prepared for three steady state 

conditions. In the same way, a combined factor of safety map will be generated. 

The accuracy of the models will be cross validated by using the inventory. 

Statistical methods will be discussed in chapter 5. A total of 12 models will be 

constructed on the basis of the causative link between landslides and triggering 

factors. 



The model outputs will be checked against the inventory to test the prediction 

capacity of the methods. 

A comparison of the model outputs, a conclusion on the best prediction and 

identification of the responsible triggering factors will be made in chapter 6. 


Chapter 2: Literature review 

Instability related issues in engineered as well as natural slopes are the common challenges to 

both researchers and professionals. In construction areas, instability may result due to rainfall, 

increase in groundwater table or change in stress conditions. Similarly, natural slopes that 

have been stable for many years may suddenly fail in response to changes in geometry, 

external forces or loss in shear strength (Abramson et al., 2002). According to Tayler & Burns 

(2005), earthquakes can be considered as the greatest threats to the long‐term stability of 

slopes in earthquake active zones. Moreover, the long‐term stability is associated with 

weathering and chemical influences that may decrease the shear strength and create tension 

cracks. In such circumstances, therefore, the evaluation of slope stability conditions becomes 

a primary concern (Prasad, 2006). 

2.1 Factors influencing slope stability 

The varied topographic features of the earth’s surface are possible only because the shear 

strength of the soil exceeds shearing stresses imposed by gravity or other loadings. Slope 

failure not only can occur in the steepest slope but sometimes occur in relatively flat slopes. 

Factors leading to instability can generally be classified based on the effect they have on 

slopes (Varnes, 1984; Yokota & Iwamatsu, 1999; Chigira, 2002). 

2.1.1 Gravity 

The main force responsible for mass wasting is gravity. Gravity is the force that acts 

everywhere on the Earth's surface, pulling everything in a direction toward the center of the 

earth. On a slope, the force of gravity can be resolved into two components: a component 

acting perpendicular to the slope and component acting tangential to the slope. On a steeper 

slope, the shear stress or tangential component of gravity increases and the perpendicular 

component of gravity decreases. Therefore, the down-slope movement of a material is favored 

by steep slope angles which increase the shear stress, and anything that reduces the shear 

strength, such as lowering the cohesion among the particles or lowering the frictional 

resistance. 


Chapter 2: Literature review 6 

2.1.2 The effect of water 

Dry unconsolidated grains will form a pile with a slope angle determined by the angle 

of repose. The angle of repose is the steepest angle at which a pile of unconsolidated 

grains remains stable, and is controlled by the frictional contact between the grains. In 

general, for dry materials the angle of repose increases with increasing grain size, but 

usually lies between about 30 and 37 o (Upreti & Dhital, 1996). 

Slightly wet unconsolidated materials exhibit a very high angle of repose because 

surface tension between the water and the solid grains tends to hold the grains in place. 

When the material becomes saturated with water, the angle of repose is reduced to 

very small values and the material tends to flow like a fluid. This is because the water 

gets between the grains and eliminates grain to grain frictional contacts. 

Another aspect of water that affects slope stability is pore water pressure. In some cases fluid 

pressure can be built in such a way that water can support the weight of the overlying 

rock/soil mass. When this occurs, friction is reduced, and thus the shear strength holding the 

material on the slope is also reduced, resulting in slope failure. Pore water pressure built up 

within a regolith is directly related to the increase in groundwater table. Presumably, such 

actions are also controlled by subsurface flow parameters i.e. precipitation, soil physical 

properties and infiltration capacity. The dynamic conditions of pore water pressure built up 

during different storm events are directly related to landslide initiations (O’Loughlin & 

Pearce, 1982; Sidle & Swanston, 1982; Sidle, 1984). 

2.1.3 Geological factors 

Translational/rotational slides generally occur in relatively cohesive, homogeneous soils and 

rocks. Landslide events are strongly controlled by the nature of the regolith material 

(Thomson, 1971; Sawnston, 1978; Yokota & Iwamatsu, 1999; Wakatsuki et al., 2005). 

Failure commonly occurs along bedrock bedding planes that are deep-seated and dip in the 

same direction as the slope surface. In saturated conditions, incompetent material may fail 

under overburden weight and high pore pressures, resulting in a deep-seated rotational-type 

failure. Translational slides commonly are controlled structurally by surfaces of weakness 

such as faults, joints, bedding planes, and contacts between bedrock and overlying deposits 

(Sidle & Ochiai, 2006). Generally slope stability is influenced by the following geological 

factors: 



‣ Type and engineering-geological properties of soils/rocks, their distribution, and the 

effect of groundwater on these properties 

‣ Geological structures such as cleavage, joints, faults and folds 

‣ Stresses in geological history 

Engineering-geological properties 

Lithological units, such as basalts, shales, sandstones and limestones have different shear 

strength characteristics because of the varying conditions under which they are formed. They 

also have different mineral constituents and fabrics. Therefore, they undergo different changes 

in strength on remolding i.e. sensitivity (Malik, 1996; Upreti & Dhital, 1996). 

Geological structures 

Geological structures, such as bedding, joints, foliation, cleavage, schistosity, and faults are 

potentially weak planes in a slope. Their strength is generally less than in the surrounding 

intact rock. It is therefore imperative to know their orientation in relation to slope angle, 

direction, and strength along such potential weak planes (Sidle & Ochiai, 2006). 

Stresses in geological history 

In addition to the geological structure and lithology, weathering plays an important role. 

Mechanical and chemical weatherings change the strength parameters of the rock and soil 

considerably. In many landslide events, chemical alterations, such as hydration and ion 

exchange in clay, are thought to have contributed to triggering landslides (Zaruba & Mencl, 

1982). Geological history is also an important factor in determining the response of materials 

to excavation or landslides. In over consolidated materials, stresses stored may not have been 

completely released at the time of slope formation and this result in an outward movement at 

the base of the slopes (Tianchi, 1990; Deoja et al., 1991; Chalise & Karki; 1995, Malik, 

1996). 

2.1.4 Soil Properties 

Both the hydraulic properties and the shear strength behaviors of soils can affect the stability 

of a slope with or without a rainstorm. Another attributed effect is the presence of soils that 

contain a high proportion of a type of clay mineral called smectites or montmorillinites. 



Such clay minerals expand when they become wet as water enters the crystal structure and 

increases the volume of the mineral. When such clays dry out, the loss of water causes the 

volume to decrease and the clays to shrink or compact. Failure is attributed with pore water 

pressure built up on such soils. 

2.1.5 The effect of vegetation 

It is well understood that vegetation influences slope stability in two ways: through 

hydrological effects and mechanical effects. Hydrological effects involve the removal of soil 

water by evapotranspiration through vegetation, which lead to an increase in soil suction or a 

reduction in pore-water pressure, hence, an increase in the soil shear strength. The shear 

strength of the soil is also increased through the mechanical effects of the plant root matrix 

system (Greenway, 1987; Anju, 2005). 

2.1.6 Triggering events 

A mass-wasting event can occur any time a slope becomes unstable. Sometimes, as in the case 

of creep or solifluction, the slope is always unstable and the process is continuous. On the 

other hand, triggering events can happen that cause a sudden instability. The triggering 

mechanisms could be shocks and earthquakes (Keefer, 2002), slope modification, 

undercutting or changes in hydraulic characteristics (De. Vleeschauwer & De Smedt, 2002). 

Table 2.1 Triggering events influencing slope stability 

Increased stresses 

1. External loads such as buildings, 

roads, etc 

Decreased Strength 

1. Swelling of clays by adsorption of 

water. 

2. Increase in unit weight by 

increased in moisture content. 

3. Removal of part of slope by 

excavation. 

4. Undermining caused by tunneling, 

collapse of underground caverns, or 

seepage erosion. 

5. Shock caused by earthquake or 

blasting. 

6. Tension cracks. 

2. Pore water pressure. 

3. Breakdown of loose or 

honeycombed structure of soil with 

shock, vibration or seismic activity. 

4. Hair cracking from alternate 

swelling and shrinking or from 

tension. 

5. Strain and progressive failure in 

sensitive soils 

6. Deterioration of cementing 

material. 

7. Water pressure in cracks. 7. Loss of capillary tension on drying 

8. Weathering – chemical or biochemical 

deterioration. 



2.2 Landslide inventory 

A clear understanding of landslide conditions and a more detailed assessment of the landslide 

hazard of the area concerned are essential to make a systematic landslide inventory. All 

failures recorded in historical and technical documents, investigated and identified or by aerial 

photographic analysis, should be registered. Compiling the locations and conditions of 

existing landslide zones is the basic requirement before performing any slope stability 

analysis (Van Westen, 1993; Van Westen et al., 2006). A landslide is a natural geologicgeomorphologic 

phenomenon in which the soil and/or rock mass resting on top of a sliding 

surface starts to slowly or rapidly move downslope because of the pull of gravity. By the time 

the landslide activity ceases, a specific topography associated with the landslides will be 

formed. A landslide inventory is, therefore, carried out to identify the newly formed 

topographic feature. The analysis of aerial photography is a quick and valuable technique for 

identifying landslides, because it provides a three-dimensional overview of the terrain and 

indicates human activities. Generally aerial photographs are used to identify specific 

geomorphic features that reflect landslide topography. Important geomorphic features are 

those associated with the failure of large, deep-seated landslides involving bedrock and thick 

soil as well as large rockslide and rockfall deposits. Tianchi et al. (2001) and Dikau et al. 

(1996) explain the geomorphic features that can be used as indicators of landsliding on 

stereoscopic pairs of aerial photographs. 

Steep crescent-shaped surface that is concave down slope, minor scarps, grabens, fault 

blocks and trees that lean uphill describe landforms in the head region of landslides. 

Relatively flat hillside areas, circular or oval hillside ponds, hillside terrain with 

transverse ridges and secondary scraps indicate interior body of a landslide. 

Crescent –shaped ridge that is convex down slope, trees that lean downhill or at 

various angles depict the foot region and zone of earth flow. 

Sometimes, landslide features are not easily recognized due to vegetation cover, alteration of 

the terrain by human activities, or surface erosion that modifies landslide features. The 

advantages of preparing inventories by interpreting aerial photographs and field examination 

are, (1) It is rather quick and inexpensive, (2) It shows where landslide processes seem to be 

concentrated, and correspondingly, it shows where more detailed studies are likely to be 

undertaken in the future (Tianchi et al., 2001). 



2.3 Anticipation of landslide hazards 

The three types of landslide hazard assessment studies important to planners, engineers and 

the public are: 

‣ Long term predictions: It is used to delineate areas that might have hazards caused by 

landslides for the purpose of land use planning, regional hazard assessment and 

preventation, urban development, and evaluation of environmental changes after 

deforestation. One important principle of the long-term prediction is that the past is the 

key to the future. This means that landslides will probably occur as a result of the 

same geologic, geomorphic, and hydrologic situations that led to past and current 

landslides. Based on this assumption, it is possible to estimate the type, frequency and 

occurrences of landslides in a study area. Making such a prediction involves carrying 

out geologic and geomorphic studies and aerial photo interpretation. It means, it 

consists of preparing regional, or reconnaissance landslide maps, such as inventory 

maps, landslide hazard maps and susceptibility maps that identify and delineate 

regional landslide problem areas and conditions in which they occur. These maps can 

also be used to predict the relative degree of hazard in a landslide area (Tianchi, 1990; 

Deoja et al., 1991; Chalise & Karki, 1995). 

‣ Geotechnical methods: These methods are used to quantitatively asses slope stability 

under man-made conditions such as changes in topography, hydrologic conditions. In 

addition, those methods are employed to analyze old landslides under anticipated 

seismic or meteorologic conditions. When making such computations, it is important 

in all aspects of the study, including geologic definitions, to understand the 

relationship between the subsurface geometry and strength parameters of the slide 

plane with natural conditions. Due to high cost, this method can only be applied to 

those slopes on which large risks are expected according to the long term conditions, 

or on which economically important engineering operations are being carried out. The 

basic characteristics of these models are based on different mathematical and physical 

models (Malik, 1996; Thakur, 1996; Upreti & Dhital, 1996). 

‣ Short term predictions: Such predictions are used for evacuation before an imminent 

large scale landslide. They are based on (1) field measurements of displacements, 

rainfall and pore water pressures (2) forerunning indicators of landslides (Tianchi, 

1990; Deoja et al., 1991; Chalise & Karki, 1995; Malik, 1996). 



2.4 GIS methods 

The recent development of GIS technology for data integration, combined with the 

availability of digital elevation models of acceptable quality to analyze geographic and 

geologic data, has greatly increased the applicability of many techniques for landslide hazard 

assessment (Brabb, 1984; Varnes, 1984; Van Westen, 1994; Carrara et al., 1995). Most of the 

conventional GIS techniques for landslide mapping are based on ‘map overlaying’, which 

only allows for the comparison of different maps on the same location and scale by placing 

them one on top of the other and using the criteria for landslide assessment. Such techniques 

for modeling slope instability have been employed by different investigators. Reviews 

outlining the methods are given by, Hansen (1984), Carrara et al. (1995), Hutchinson (1995), 

Soeters and Van Westen (1996), Van Westen et al. (1997), Long (2008). The methods for 

ranking slope instability factors and assigning different susceptibility levels can be divided 

into: 

‣ Qualitative or quantitative: Qualitative methods are subjective. They establish 

susceptibility heuristically, and portray susceptibility levels using descriptive 

(qualitative) expressions. Such techniques depend highly on experience, knowledge 

and previous works on the study area. It is based on how well and how much the 

investigator understands the geomorphological processes acting upon the terrain. On 

the contrary, Quantitative methods produce numerical estimates, i.e. probabilities of 

the occurrence of landslide incident in any susceptibility zone (Guzzetti et al., 2005). 

‣ Direct mapping methods are those that recognize the spatial distribution of landslides 

directly from existing failure zones or using specific knowledge of areas of potential 

instability. A direct method consists of geomorphological mapping of landslide 

susceptibility in the field, using aerial photographs or from satellite images 

(Verstappen, 1983; Nossin, 1989). It still relies on the ability of the investigator to 

estimate actual and potential slope failures. Indirect mapping techniques are those that 

employ causative relationship based on possible triggering factors to estimate 

potential instability. Indirect methods for landslide susceptibility assessment are 

essentially stepwise. Guzzetti et al. (2005) determined the requirements of Indirect 

methods as: 

Recognition and mapping of landslides over a target region or a subset of it 

(i.e. the training area), which is obtained by preparing a landslide inventory 

map. 



 

Identification and mapping of the physical factors which are directly or 

indirectly correlated with slope instability (the triggering factors). 

 

An estimate of the relative contribution of the triggering factors in generating 

slope failures. 

 

Classification of the land surfaces into domains of different levels of 

susceptibility. 

 

Assessment of the model performance (validation). 

Table 2.2 Characteristics of landslide susceptibility methods proposed by Van Westen et al. (1997a) 

Direct Indirect 

Qualitative Quantitative 

Geomorphological mapping 

Heuristic (index-based) 

Analysis of inventories 

Statistical modeling 

Process based (conceptual) 


Chapter 3: Methodology and data preparation 

3.1 Landslide Inventory mapping 

Landslide inventory maps show locations and characteristics of landslides that have moved in 

the past but generally do not indicate the mechanism(s) that triggered them. The geologic, 

topographic and climatic conditions that led to past slope failures often provide clues to the 

locations and conditions of future slope failures. Therefore, inventory maps provide useful 

information about the potential for future landsliding. It is the most straightforward initial 

approach to any landslide hazard study and such inventories are the basis for most 

susceptibility mapping techniques (Dai et al., 2002). In addition, recognizing the type and 

recency of landsliding can also facilitate the scope and design of site-specific geotechnical 

investigations and guide slope remediation strategies. 

Inventory maps are prepared primarily by geomorphic analysis of aerial photographs and 

secondarily by field reconnaissance, interpretation of topographic map contours and review of 

previous mappings. 

Detailed geomorphological investigations (Nyssen et al., 2002; Moeyersons et al., 2006, 

2008) were needed to realize that landslides and related mass movement topography occupy 

an important part of the landscape in the Hagere Selam area. 

Moeyersons et al. (2008) distinguishes several mass movements. The first type is rockfall 

produced by detachment and toppling during the rainy season from 357 km rocky 

escarpments and cliffs with a height of more than 10m. The rock fragments tumble, topple 

and roll down the valley, sometimes to about 100m. A detailed inventory in the study area 

indicates that rockfall alone is responsible for a cliff recession rate up to 3.7 cm/century which 

is 3.7m³ of rockfall per annum (Nyssen et al., 2006). Three dormant debris flows are also 

recognized. The first type includes the preferential mobilizations of the geologic layers resting 

over the Amba Aradom sandstone, the plateau layers. It includes the lower and upper trap 

basalt layers, sandwiched whitish sandy-clayey lacustrine deposits, and the expansive clays 

obtained from the weathering of the basalts. These landslides dominantly occur below the 

tabular extensions of Amba Aradom sandstone and flow over the cliff and descend very far 

down in to the valley. Sometimes, these debris flows include detached fragments of the lower 

layers, Amba Aradam sandstone and Antalo limestone. 


Chapter 3: Methodology and data preparation 14 

The other, rather uncommon, debris flow occurs in the Antalo supersequence. It is attributed 

with the presence of massive and hard layers which act like aquitards/aquicludes that block 

the passage of water to lower layers and thus result in pore water pressure built up. Finally, 

gigantic debris flows and rock slides occur around dolerite dyke ridges resulting in deposition 

of fresh dolerite rock boulders over a typical corestone weathering profile (Moeyersons et al., 

2008; Twidale, 1982). 

For this study, major geomorphic and geologic features are interpreted using aerial 

photographs with a scale of 1:50,000 (Ethiopian Mapping Authority, 1994) and a 

topographical map of Hagere Selam area on a scale of 1:50,000 (Ethiopian Mapping 

Authority, 1996). Site investigation results obtained from previous studies, Nyssen et al. 

(2002, 2006), Moeyersons et al. (2006, 2008) are thoroughly assessed. Digital photographs 

obtained from the metadata of the Royal Central Africa museum are exploited to build the 

relationship between the recognized features of the aerial photographs with the reality. 

The aerial photographs which were used for this study are ETH94:R2/26 +0789, +0790, 

+0791, ETH94:R2/27 +0826, +0827, +0828, +0829, +0830, and ETH94:R3/28 +0864, +0865, 

+0866, +0867. 

Moeyersons et al. (2008) identified 28 debris flows, 7 landslide belts, 6 debris and rock slides, 

5 landslides in the Antalo supersequence and 10 landslides in Imba Degua and Chini ridges. 

In this study, according to the interpretation, we produced a landslide inventory map 

consisting of cliffs associated with rock falls and landslides zones (debris flows, debris slides, 

rock slides and slumps). 

We identified 30 debris flows, 7 landslide belts, 4 landslides in the Antalo supersequence, 12 

landslides in Imba Degua and Chini ridges and 2 slumps. However, the debris slides and small 

rock slides observed by Moeyersons et al. (2008) are too small to be recognized by the aerial 

photographs. 



Figure 3.1 Landslide Inventory map 

Details on the slides are shown in Figure 3.2. (A) landslide in Tsilli ridge, (B) May Antebteb debris flow, (C) Landslide belt around 

Medayk ridge, (D) Melka Maryam debris flow, and (E) Upper Tankwa river valley (Tsatsen ridge) 



Figure 3.2 Photographic views of some slides (Source: Online database of Royal Central 

Africa museum and Moeyersons et al., 2008) 



3.1.1 Mechanism of failures 

A. Debris flow affecting plateau layers 

Figure 3.3 Landslide mechanism 1 (Source: Moeyersons et al., 2008) 

Geologic layers resting over Amba Aradom sandstone which are the Upper basaltic layer, the 

whitish sandy clay lacustrine deposit, the lower basaltic series and the weathering product of 

the basalts i.e. swelling clays start flowing as a massive surge from a nearly horizontal Amba 

Aradom structural surface and descend down the valleys. Amba Aradom sandstone is an 

aquicludes/ aquitard which could sufficiently block the passage of water (Tesfaye and 

Gebretsadik, 1982). The increase in water content of the upper layers can mobilize the 

landslides in two ways: (1) By destroying the natural anchorage between particles and 

decreasing the shear strength of the materials (2) By developing pore water pressure and 

increasing the shear stress acting on the plateau layers. The above Figure indicates that the 

flows are sometimes mixed with detached fragments of Antalo limestone and Amba Aradom 

sandstone. 

B. Landslides affecting the Antalo supersequence. 

Most landslide failures in Ethiopia are rainfall triggered. The landslides affecting Antalo 

limestone are also related with pore water pressure built up above massive/ hard layers acting 

as an aquicludes or aquitard. Figure 3.4 shows the mechanism by which failure occurs. 



Figure 3.4 Landslide mechanism 2 

The first massive formation recognized as an aquitard or aquiclude is the Adiwerat layer (Van 

de Wauw et al., 2008). It is a massive shale layer on the top of which landslides of Agula 

shale would be mobilized. Following these movements the cliffs resting over the Agula shale 

will start sliding. Thus, these flows are characterized with sandstone and basalt inclusions 

(Van de Wauw et al., 2008). The second aquitard is the upper shale or marl layer which is also 

called May Ba’ati aquitard (Moeyersons et al., 2008). It has a low small hydraulic 

conductivity and water can hardly move to lower layers. However, the carbonate layer resting 

over the shale, Storamatoporoid bed, has a considerable vertically permeability resulting from 

cracks. Thus water is allowed to stand in the cracks. When the water table is increased to a 

level higher than the Storamatoporoid layer, pore water pressure will be built up in the upper 

Anatalo limestone and will mobilize the landslides. The third aquitard is the lower shale/ marl 

layer under carbonate cliffs of stromatoporoid layer. The mechanism of failure is similar to 

what is explained for the upper series. 

C. Landslides around Imba Degua and chini 

The geologic composition of the Imba Degua and Chini ridge is quite different from what has 

been observed in the rest of the study area. Most part of the ridge is composed of dolerites. 

Unlike other places, the landslides around this ridge are not characterized by structural 

topography but are composed of fresh dolerite boulders resting over corestone weathering 

profiles. These slides are huge enough that the lobes are clearly identified by aerial 

photographs. However, field observations reveal that the area is not characterized by rugged 



topography (Moeyersons et al., 2008). The landslide depletion areas are not completely 

identifiable with aerial photograph interpretation. Some erosional belts visibly start from the 

steepest parts of the ridges while other lack morphological justification to delineate the zones. 

However, Moeyersons et al. (2008) state a field recognition result with a significant difference 

in size and freshness of dolerite boulders with the weathering profiles. Thus the erosional 

belts are considered to start from the steepest parts of the ridges. 

D. Rock falls 

A landslide analysis in Hagere Selam area will not be complete without including the effect of 

rock falls. The failure mechanism is believed to be detachment and toppling of boulders from 

rocky cliffs after every rainy season. In addition, a secondary movement of the rocks has to be 

considered. Creep effects are not easily realized in short period of time, but are very 

devastating as the attachment between the rocks particles will get removed with time. From 

1998 to 2001, slopes below Amba Aradom sandstone have been investigated twice a year, at 

the end of the rainy season and mid of the dry season (Nyssen et al., 2006). The investigation 

includes identification of down slope scars of damaged vegetation, freshly fallen boulders and 

information of local shepherds. For creep considerations, theodolite measurements have been 

collected (Nyssen et al., 2006). 

For this study, we identified the cliffs using the topographic map and the DEM. It is intended 

to apply infinite slope and statistical models for the landslide identification. However, the 

Mohr – Coulomb criterion cannot be used because of the extremely high cohesion of 

consolidated or cemented rocks as compared to soft materials. So, the shear strength 

parameters of weathered rocks will be treated. Landslides can be detected in cliffs due to 

their large slope angles; despite they have very large shear strength. As a first approximation, 

the infinite slope analysis will be applied. Alternatively, Statistical models are expected to 

yield reliable results to identify rock falls, as their consideration is causative not inherent soil 

properties or particular failure plane assumption. 



3.2 Geotechnical parameters 

In slope stability analysis, evaluation of variables such as soil stratification and in situ shear 

strength parameters may prove to be a formidable task especially when the analysis concerns 

to be spatial. The inherent soil parameters would be difficult to obtain on a detailed scale in 

regional basis. In that instance, grouping of adjacent soil units having homogeneous 

properties would be practical. Direct measurement of the parameters would then be made in a 

representative number of times for each unit. In case when direct measurements do not exist, 

range of literature values could be tried and a reasonable decision would be taken. Most of the 

time, results of laboratory analysis do not happen to be better than literature values. 

There were two possible ways to obtain the geotechnical parameters of the study area: (1) 

considering the FAO soil map 1998, (2) considering the lithology. As The FAO soil map 

1998 on the study area is compiled with a large scale and would give a rough approximation, 

it is concluded to use the lithology as the base for major soil properties. The areal distribution 

of the geologic formations is shown in Figure 3.5. 

Entcho Sandstone 

Endaga Arbi Glacial 

Alluvium 

Adigrat Sandstone 

Marl Limestone 

Shale Marl Limestone 

Dolerite Sill 

Shale 

Amba Aradom Sandstone 

Limestone Marl 

Trap basalt 

Areal distribution of geologic layers(%) 

0 5 10 15 20 25 

Figure 3.5 Areal distribution of geologic layers 

The geologic properties of these formations are described, in short, here below: 

1. Trap basalts: are represented by dark colored, fine to coarse grained, basic igneous 

rocks that are associated with soft sediments (volcanic ash and/or lacustrine deposits) 

as discontinuous interbeds. 



2. Horizontal/sub-horizontal flow structures form rock sequences of variable 

characteristics which range from well developed columnar joints to less fractured and 

massive layers. The majority of soils derived from basalt flows are grouped into CH, 

inorganic clays of high plasticity, SC-clayey sand, and MH-inorganic silts 

(Woldaregay et al., 2006). 

3. Antalo limestone: It is represented by yellow to white, slightly to moderately jointed 

limestone with intercalations of calcareous clay (Marl). Limestone to marl ratio is not 

constant throughout the area. The marl interbedding is greatest at escarpments, 

decreasing considerably both to the east and west. 

4. Amba Aradom sandstone: It is characterized by fine to medium grained, white to pink 

colored, thickly bedded, slightly to moderately weathered sandstone, which is 

interbedded with thin layers of siltstone/ claystone. Vertical/sub-vertical joints and 

horizontal/sub horizontal bedding planes are characteristic of these rocks. 

5. Agula shale: is dominated by gray, green and black, thinly bedded, moderately to 

highly weathered shale (marly), which is interlaminated with thin limestone beds. 

Soils derived from Agula shale range from CH, inorganic clays of high plasticity, to 

ML, inorganic silts and very fine sands with slight plasticity. 

6. Dolerites (resting on a typical corestone weathering profile): These rocks include a 

variety of intrusive bodies which form isolated peaks and ridges. In steep terrains they 

are affected by vertical/sub-vertical tensional fractures (Twidale, 1982). 

7. Adigrat sandstone: Is a medium to coarse grained cliff forming sandstone with some 

shale and laterite intercalations. It is characterized by red to brown, slightly to 

moderately weathered, horizontally bedded, jointed sandstone. These rocks are 

associated with vertical/ sub-vertical joints and horizontal/sub horizontal bedding 

planes. In many cases sandstone cliffs are associated with open tensional fractures 

(Woldaregay et al., 2006). 

8. Alluvium: are unconsolidated deposits overlying the different rocks. 

9. Edaga Arbi glacial: The glacial are mainly composed of clay and silt and act like an 

aquicludes. 

10. Enticho sandstone: It is a white fine to medium grained sandstone with calcareous, 

locally siliceous, cement which is leached at the surface. The sandstone shows 

variations in grain size, sorting and degree of cementation. It contains clay and lithic 

materials. 



The lower part of the sandstone is massive while the upper is cross-bedded (Tesfaye & 

Gebretsadik, 1982). 

Nyssen et al. (2002) explain the shear strength measurements conducted in the laboratory, 

using a manual monoaxial shear strength measuring device (M&O, Montrouge, France). 

Disturbed soil samples taken from toe and sides of the lobes are first saturated with water and 

then consolidated under a normal pressure of 59 kN/ m² in an attempt to simulate a soil depth 

of +3 m, which is similar to the thickness of the May Ntebteb flow where it leaves the Amba 

Aradam sandstone cliff. Water is allowed to be absorbed by the soil samples till field capacity 

was reached in equilibrium with the attained state of consolidation. The samples are then 

submitted to shear tests at normal stresses of approximately 60, 40, 20 and 0 kN/m². Slow 

tests with a shear stress increase of 981 N /m² in 3 min have been conducted in order to assure 

a drained test 

Table 3.1 Shear strength parameters 

θ field 

Site Composition capacity γ field capacity (kN/m³) Cohesion(kPa) Friction angle(°) 

May Ntebteb Swelling clays 0.471 15.77 6.18 24 

Amba Raeset Weathered Marl 0.263 17.67 11.58 20 

The dry unit weight can be calculated by 

γ = 

γ 

1 + w 

(3.1) 

Where γ d = dry unit weight 

w = water content (weight of water per weight of solid material) 

γ = unit weight at a water content of w 

Woldearegay et al. (2006) also performed measurements on the shear strength of weathered 

Basalt and Agula shale. The result is reported in Table 3.2. 

Table 3.2 Range of shear strength parameters for weathered formations 

Formation Cohesion (kPa) Friction angle (°) γ sat (kN/m³) 

Weathered Basalt 5 to 32 16 to 28 17 to 22 

Weathered Agula Shale 16 to 37 15 to 27 16 to 23 

The analysis of slope stability could not be realistic, if performed on a particular value of 

cohesion, friction angle or unit weight. 



Those soil parameters are highly variable with space and time. Yet the range of the values 

remains, almost, reasonably known. One of the particular capabilities of GIS is its ease to 

change the parameters and check different combinations. Thus this study is intended to be 

performed using different values in the range and come up with a reasonable approximation. 

The parameters for landslide mechanisms 1 and 2, described before, are reasonably available. 

These flows involve basaltic, marly limestone and shale materials. For other formations, 

landslides will occur at joints and fractured zones. Thus, ranges of strength parameters are 

looked for from different literatures. Franklin & Dusseault, (1989), and Jaeger & Cook, 

(1977) compile friction angle for rock joints and fillings. Barton, (1973), and Hoek & Bray, 

(1977) also give range of friction angle values for residual soils. The values are complied in 

Table 3.3. Other literatures also give values related to weathered formations (Sloane, 1991; 

Hudson et al., 1997; Hoek et al., 1998; Văn et al., 2001; Kentli & Topal, 2004). 

Table 3.3 Friction angle values for rocks at joints 

Franklin & Dusseault (1989), 

and Jaeger & Cook (1977) 

Barton (1973), and Hoek & 

Bray (1977) 

Jointed rocks φ' (°) Jointed rocks φ' (°) 

Porous limestone 32-48 Basalt 31-38 

Sandstone 24-35 Limestone 33-40 

Clay shale 22-37 Sandstone 25-35 

Dolerite 33-43 Shale 27 

Another approach for these lithologic layers is to consider the shear strength parameters of 

their weathering products, soils derived from the formations. Lambe & Whitman, (1979) give 

typical values as described in Table 3.4 and 3.5. 

Table 3.4 Angle of friction and unit weight values of soils derived from geologic layers 

Soil type Friction angle (°) γ sat (kN/m³) γdry(kN/m³) 

Sandy loam 34 20.2 17.3 

Loam 33 18.6 14.9 

Clay 28 19.9 16.5 



Table 3.5 Cohesion and friction angle values of soils derived from geologic layers 

Soil Cohesion (kPa) Friction angle (°) 

Silt (non plastic) 0 26-30 

Uniform fine to 

medium sand 0 26-30 

Well graded sand 0 30 -34 

Sand and Gravel 0 32 -36 

Roughly equal to non 

Predominantly clayey 4.8-24 consolidated soil 

Driscoll, (1979), cited by Deoja, (1991) also state some shear strength parameter values as given 

in Table 3.6. 

Table 3.6 Cohesion and friction angle values of soils 

Soil type Symbol Cohesion (kN/m²) Friction (°) 

Well Graded Gravel GW 0 >38 

Poorly Graded Gravel GP 0 >37 

Gravel with Silts GM - >34 

Gravel with Clay GC - >31 

Well Graded Sand SW 0 38 

Poorly Graded Sand SP 0 37 

Sand with Silts SM 2 to 5 34 

Sand with Clay SC 1 to 7.5 31 

Mixture of sands with silts 

and sands with clay 

SM-SC 1.5 to 5 33 

Inorganic Silts ML 1 to7.0 32 

Inorganic Clay CL 1.5 to 9 28 

Mixture of inorganic silts and 

inorganic clay 

ML-CL 2 to 6.5 32 

Organic Silts OL - - 

Inorganic Silts MH 2.1 to 7.5 25 

Inorganic Clay CH 1 to 10 19 

For this study, we concluded to try different combinations of values using the laboratory 

results as well as literature values. The one yielding the most unstable condition will be 

considered to be governing. Thus we come up with a final compilation of shear strength 

parameters which is given in Table 3.7. 



Table 3.7 Final compiled shear strength parameters 

Weathered Formation 

Weathered Basalt 

(Swelling clays) 

Weathered Marl 

limestone 

Cohesion (kPa) Friction angle (°) γ sat γ dry 

C' φ' (kN/m³) (kN/m³) 

6.18 24 15.77 10.72 

11.58 20 17.67 13.99 

Remark 

On the study area by Nyssen 

et al. (2002) 

On the study area by Nyssen 

et al. (2002) 

Weathered Sandstone 0 24 23 21 

Table 3.3 and Sloane, 

(1992), Hudson et al. (1997), 

Barton (1973) 

Weathered Shale 

4.4 19 18.2 15.1 

Table 3.2 and Văn et al. 

(2001) 

Weathered Dolerite 0 33 34 28.44 Table 3.3 and (Sloane, 1992) 

Weathered Shale -marl 

limestone 

5.79 21 20.84 18.84 

Alluvium 23 25 18 16 

Hoek et al. (1998), Kentli & 

Topal (2004) 

Table 3.4, 3.5, 3.6 and Văn 

et al. (2001) 

3.3 Soil depth 

Maps of the soil depth on steep hillsides are required for deterministic shallow landslide 

models that include the effects of infiltration or soil cohesion. However, since collecting 

sufficient measurements to map soil thickness compatible with the scale of the DEMs is 

practically impossible; deterministic modeling efforts have typically relied on empirical or 

theoretical models to create soil depth maps (Dietrich et al., 1995; DeRose, 1996; Casadei et 

al., 2003). 

Ray & De Smedt (2009) determined the thickness of soil based on land use and corrected it 

for slope angles, because depth of soil is inversely proportional with steepness of a terrain. 

Salciarini et al. (2006) also used an exponential function of topographic slope to map the soil 

depth in slope stability calculations. 



De Rose (1996) and Salciarini et al. (2006) estimate the depth to the lower boundary as: 

d 7.72* e 

0.04 

(3.2) 

lb 

Where d lb = depth to the lower boundary; 

β = slope angle; 

d lb is at a minimum of about 0.5 m on the steepest slopes, about 1.4 m on 40° slopes, 

and about 2.0 m on 30° slopes. 

Such approximations are expected to be used as a first estimate in slope stability calculations, 

and they are not considered to represent the actual site condition. The ideal investigation 

would be creating borehole locations and compiling geological, geotechnical and soil depth 

parameters. A site specific empirical relation is needed to be developed using topographical 

parameters and the borehole data. 

A shallow depth results in greater discharge volume and lower peak pore water pressure 

during major rainfall events, and consequently slope failure tends not to occur. However, a 

deeper material depth increases the weight of solids and the soil moisture in a slope and the 

pore water pressure. Hence it increases the likelihood of slope failure. 

Even though the thickness of the soil is an important parameter, it does not have an impact in 

granular soils. For cohesionless soils, the stability of slopes depends only on friction angle 

and slope angle in the dry conditions. And, for half saturated or saturated scenarios, the 

stability is controlled by unit weight, friction angle and slope angle values. This will be 

discussed in detail in chapter 4. 



3.4 Causative factor maps preparation 

3.4.1 Slope factor 

On steeper slopes, the tangential component of the weight of a mass and the shear stress 

increases while the perpendicular component of the weight decreases. The resistance to the 

down slope movement is favored by the frictional resistance and cohesion among the particles 

that make up the object. When the shear stress is greater than the combination of forces 

holding the object on a slope, the object will tend to move down-slope. Thus the slope of a 

terrain is a principal factor involving in the analysis of landslides. 

The slope map of the study area is extracted using IDRISI GIS from the digital elevation 

model prepared using SRTM with pixel size of 90m. The minimum slope angle along flat 

sections is 0° while the maximum slope observed on nearly vertical cliffs is 57°. 

A slope category map is prepared for five categories: (1) Flat slopes (0-5°), (2) Rolling terrain 

(5-15°), (3) Mountainous, (15-25°), (4) Escarpments (25-45°), and (5) Cliffs (45-60°). The 

classification indicates that 52.42% of the study area is dominated by a rolling terrain, while 

Flat terrain amounts for 22.37%, 17.74% of the area is characterized by a Mountainous 

terrain, Escarpments occur on about 4.96% of the area and Cliffs occupy 0.1%. 

Figure 3.6 Slope map 



3.4.2 Geological factor 

The Distribution of landslide is largely controlled by bedrock geology. Rock formations and 

the type of soil present control the shear strength that can be provided, the weight of a mass 

that is involved, and the hydrologic characteristics of the regolith. The mechanical, 

mineralogical and hydrologic properties of the weathering products are directly dependent on 

the lithology. 

The geology of Hagere Selam area covers the north-western part of the Mekele outlier, which 

is composed of a nearly sub horizontal succession of the geological layers consisting of a 

lower sandstone unit, the Adigrat sandstone, an intermediate large carbonate unit, the Antalo 

limestone and Agula shale, and an upper sandstone unit, the Amba Aradam Formation 

(Bosellini et al., 1997). The Paleozoic Formation includes the Edaga Arbi glacial and Enticho 

sandstone which are found in North West part of the study area. Enticho sandstone, the metasedimentary 

Tembien Group and the meta-volcanics Tsaliet Group are equally significant in 

studying the geologic Formation of the study area. It is a white fine to medium grained 

sandstone with some calcareous cement (Tesfaye & Geberetsadik, 1982). 

The oldest geological formation, deep in the valley of the May Zegzeg, Tsaliet and upper 

Tankwa rivers is the Upper-Palaeozoic Adigrat sandstone. It is overlain by the marine Antalo 

limestones of Jurassic age, about 500 m thick (Moeyersons et al., 2008). Agula shales, which 

form the upper part of the Antalo supersequence (Bosellini et al., 1997) are present in a small 

belt around the Imba Degoa–Amba Raeset and on the elongated pass between the latter and 

the Medayk Ridge (Moeyersons et al., 2008). Agula shales and Antalo limestones are 

truncated by a peneplanation disconformities (Dramis et al., 2002), overlain by Amba Aradam 

sandstone of Cretaceous age and by two series of Tertiary volcanic basalts. The basalts are 

intercalated with silicified lacustrine deposits (i.e. white silicified clays and marls with 

abundant cherts; Bosellini et al., 1997; Asrat, 2002). Tertiary basalts are believed to be more 

prone to mass wasting than the underlying formations (Moeyersons et al., 2008). As a result 

of contact metamorphism, the top of the Amba Aradam sandstone is resistant and impervious. 

A network of Mekelle dolerite sills and dykes are developed more or less contemporaneously 

with the plateau basalts (Dramis et al., 2002). The Imba Degoa Ridge is, for example, a feeder 

dyke of the Mekele dolerite sill (Van Den Eeckhaut et al., 2009). 

The geological map of Hagere Selam is derived from the geological map of the Mekele outlier 

on scale 1:100.000 (Russo et al., 1999), the geological map of Ethiopia, and SWIR images. It 



was recompiled and simplified to a GIS format for the purpose of landslide susceptibility 

analysis. Figure 3.7 shows the simplified geological map of the study region. 

Figure 3.7 Geologic map 

3.4.3 Distance to faults factor 

Tectonics contributes to slope instability by fracturing, faulting, jointing and deforming 

foliation structures (Ibetsberger, 1996). Varnes (1984) concluded that the degree of fracturing 

and shearing plays an important role in determining slope stability. The Effect of faulting is 

detachment of soil grains resulting in decrease in shear strength. 

The distance to faults factor is obtained using the following procedures: 

The faults on the geological map of Mekele outlier on scale 1:100.000 (Russo et al., 

1999) are digitized using IDRISI-GIS. 

Distance to those faults is calculated, and categorized into 6 groups: (1) Very close to 

faults (5000 m) 

Several faults cross the study area. SE-NW facing faults being are dominant. 



Figure 3.8 Distance to faults map 

3.4.4 Elevation factor 

The influence of elevation on the mechanism of land sliding is often attributed to be indirect. 

Humidity, hydrate reactions, weathering processes, erosion and resulting weathering depths 

are affected by elevation. The more intense erosion and weathering, the more will be the 

influence of elevation on landslides. Thus considering elevation as one of the causative 

factors is reasonable from the perspective of other elevation affected processes that control 

landslides. 



The digital elevation model representing Hagere Selam area is obtained from SRTM DEM at 

a resolution of 90 m. An appropriate filtering operation is applied on IDRISI-GIS to obtain a 

smoothened output. The study area is characterized by high relief variability ranging to about 

1158m with the minimum elevation recorded being 1668 m in the north west part of Hagere 

Selam and the maximum elevation being 2826 m around the Medayk ridge. 

For landslide susceptibility analysis, the elevation of the study area is categorized into five 

classes: (1) Low Elevation (


The effects of vegetation cover on the hydrological processes of shallow land sliding can be 

subdivided into the loss of precipitation by interception, removal of soil moisture by 

evapotranspiration and the effects on hydraulic conductivity (Van Beek, 2002). 

For the study area, we are unable to find an already prepared land use map. Compiling such a 

map requires precise imagery as well as field measurements. In fact, it is not under the scope 

of this study to produce a detailed land use map. However, we are interested to investigate the 

effect of vegetation to slope stability. This can, roughly, be obtained using SRTM (Shuttle 

Radar Topography Mission) imagery. A false color composite is compiled using VNIR image 

band 3, 2 and 1. A supervised classification is carried out controlled by training pixels 

obtained from Google earth. Identifying vegetation is rather easy on false color composite, but 

there are sometimes clear 'shading' effects that complicate the classification. In addition, there 

are rivers and other water bodies in the scene, which might have been recognized by their 

shape. However, they are difficult to be used as training sites as they are quite narrow at this 

resolution. Bare soils and Sandy soils are also identifiable in the scene. There is no big city 

that can be assigned as urban class, and hence identified as such by using its spatial structure. 

However, Hagere Selam is included using manual delineation on the classified image. The 

classification employed is a maximum likelihood classification. 

Needless to say, what we presented in Figure 3.10 doesn’t represent the land use map of the 

area but some land use features that can be identified using the available data. 

Figure 3.10 Land use factor map 



3.4.6 Slope Shape 

Degree of saturation of a slope-forming material is another major factor controlling the 

occurrence of landslides. Concave slopes favor pore water pressure build up than straight 

slopes (Dai & Lee, 2002). Easy flow of water on convex slopes attribute to its stability than 

other slope shapes. For the study area slope shape map is produced using IDRISI-GIS. 

Appropriate grouping is performed to classify the map in to 7 categories: (1) Peak and ridges, 

(2) Saddle, (3) Slope hill, (4) Convex, (5) Concave, (6) Gully, and (7) Other. 

3.4.7 Aspect 

The amount of precipitation received on a particular slope differs with respect to the various 

orientations it could have. Aspect related parameters such as exposure to sunlight, drying 

winds, rainfall (degree of saturation), and discontinuities may control the occurrence of 

landslides (Dai & Lee, 2002). However, the direct relationship with aspect and landslides is 

not yet clear (Van Westen et al., 2006). The aspect map of Hagere Selam area is, thus, derived 

from the DEM to account for the probable causative relationship between aspect and slope 

instability. The aspect map is classified into 10 groups: (1) Flat (-1°), (2) North (0°-22.5°), (3) 

North east (22.5°-67.5°), (4) East (67.5°-112.5°), (5) South east (112.5°-157.5°), (6) South 

(157.5°-202.5°), (7) South west (202.5°-247.5°), (8) West (247.5°-292.5°), (9) North west 

(292.5°-337.5°), and (10) North (337.5°-360°). 

Figure 3.11 Slope shape and Aspect 



3.4.8 Distance to streams 

An important parameter that controls the stability of a slope is the degree of saturation of the 

material on the slope. The closer the slope is to streams, the more likely will it get water and 

develop pore pressure. Streams may adversely affect stability by eroding slopes or by 

saturating the lower part of the material which results an increase in ground water level. 

A map of distances to streams was calculated using the buffering algorithms of ILWIS 

software on the basis of rivers and streams available on the topographic map. The map is then 

classified into 6 categories: (1) Very close (


Figure 3.12 Distance to streams 

Figure 3.13 Distance to roads 



3.4.10 Wetness index 

In order to express water condition, a DEM-based wetness index, otherwise known as 

compound topographic index was used to represent the spatial distribution of water flow 

across the study area. The wetness index represents a theoretical measure of the accumulation 

of flow at any point within a river basin and is calculated using the expression: 

w = ln( A tanβ 

) (3.3) 

Where w is the wetness index, As is the upstream catchment area and β is slope angle. This 

equation assumes steady-state conditions and uniform soil properties (i.e., transmissivity is 

constant throughout the catchment and equal to unity). This equation predicts zones of 

saturation where the upstream catchment area (As) is large (typically in converging segments 

of landscapes), the slope (β) is small (at the base of concave slopes where slope gradient is 

reduced) and the transmissivity is minimal (on shallow soils). These conditions are usually 

encountered along drainage paths and in zones of water concentration in landscapes (Wilson 

& Gallant, 2000). The topographic wetness index map of the study area is prepared using 

IDRISI-GIS and classified into 4 categories: (1) w

Chapter 4: Deterministic models 

4.1 Physical based models 

All limit equilibrium methods utilize the Mohr-Coulomb expression to determine the shear 

strength (τ ) along a sliding surface. According to Prasad (2006), a state of limit equilibrium 

exists when the mobilized shear stress (τ) is expressed as a fraction of the shear strength. At 

the moment of failure, the shear strength is fully mobilized along the failure surface when the 

critical state conditions are reached. 

4.1.1 Stability of infinite slopes 

Figure 4.1 (a) and (b) - infinite slope force diagrams 


Chapter 4: Deterministic models 38 

Figure 4.1 shows an infinite slope with force diagrams. Assuming a seepage through the 

material and that the ground water table is at mH distance above the failure plane. The shear 

strength of the material is given by, 

τ 

f 

c' σ' tan' 

(4.1) 

Where c’ is the effective cohesion (kN/m²), σ’ is the effective normal stress (kN/m²) and ϕ’ is 

the angle of internal friction (°). 

To determine the factor of safety against failure along plane AB, we consider the slope 

element abcd. The forces that act on the vertical faces ab and cd are equal and opposite. The 

total weight of the slope element of unit length is, 

W 

L 

((1 m) 

 

dry 

m 

sat 

) H 

(4.2) 

cos 

where, γ sat = saturated unit weight of the material (kN/m³), γ dry = dry unit weight of the 

material (kN/m³), H is depth of the material above the failure surface (m), m = wetness index, 

L is length of slope element abcd (m), and β is the slope angle (°). 

The components of the weight in the directions normal and parallel to the plane AB are 

N a = Wcosβ = (( 1 m) 

m 

LH , T a = (( 1 

m) 

m 

) LH tan 

dry sat 

) 

dry 

 

sat 

 

N r =N a, T r = T a 

The normal stress is given by: 

The shear stress is given by: 

N ((1 m) 

 

dry 

m 

) LH 

r 

sat 

 

 

Area of the base L 

cos 

(( 1m) 

dry m 

sat ) H cos 

(4.3) 

T ((1 m) 

 

a 

 

 

Area of the base 

dry 

m 

sat 

L 

cos 

) LH tan 

(( 1 

m) 

m 

) H sin 

(4.4) 

dry 

 

sat 



The effective normal stress is given by: 

' u 

(4.5) 

where, u is the pore water pressure (kN/m²). Referring to Figure 4.1 (b), we find 

u 

w w 

= (height of piezometer head at f) * = h 

u = h 

w 

( ef 

w 

cos ) mH 

w 

cos 

Substituting the value of σ and u into εq 4.1, 

 

f 

c' 

(((1 

m) 

m 

) H cos mH cos ) tan ' 

(4.6) 

dry 

sat 

w 

Expressing the factor of safety as a the ratio of stabilizing to destabilizing forces, 

F 

s 

 

f 

c' 

(((1 

m) 

 

dry 

m 

sat 

) H cos 

wmH 

cos ) tan ' 

 

(4.7) 

 

((1 m) 

m 

) H sin 

dry 

sat 

Now, we introduce the effective unit weight of the material, 

( 1 

m) 

m 

e dry sat 

(4.8) 

Thus the factor of safety is written as, 

C' 

( 

m 

w 

) H cos tan ' 

F 

e 

s 

 

H sin 

e 

C' 

 

w tan ' 

Fs 

(1 m ) 

H sin tan 

(4.9) 

e 

e 

The safety factor map calculated using Eq. 4.9 can be classified into categories describing 

degree of instability. In this study, the classification shown in Table 4.1 is adopted. 



Table 4.1 Classification of factor of safety values 

Criteria 

FS


Figure 4.2 (a) saturated unit weight map, (b) dry unit weight map 



Figure 4.3 (a) cohesion map, (b) angle of internal friction map 



4.1.3 Critical depth 

The critical engineering depth is used as an indication of how a slope behaves without support 

and explains the ability of a slope to withstand instabilities. In addition, it guides the extent by 

which construction cuts and embankments can be accomplished. The Critical height map is of 

equal importance as factor of a safety map to engineers and planners. It is calculated for the 

factor of safety value of one. From equation 4.9, it can be derived as: 

H 

c 

C' 

 

2 

4.10 

sin cos ( m 

) cos tan ' 

w 

H 

c 

C' 

 

2 

4.11 

cos ( (tan tan ' 

) m 

tan ' 

) 

w 

For granular material, the cohesion between particles is negligible and C’ = 0. From equation 

4.9, the factor of safety equals tanϕ’/ tanβ for dry conditions. Hence, the slope is stable as 

long as β < ϕ’. For half saturated and saturated scenarios, the factor of safety depends on unit 

weight, friction angle and slope angle. Therefore, the stability of an infinite slope in 

cohesionless material is independent of depth. The critical depth calculation is carried out 

only for non granular materials in the study area. Three steady state conditions, m = 0, m = 

0.5 and m = 1 are considered. 

4.1.3.1 Saturated critical depth 

The calculation of critical height with equation 4.11 results in negative values of the critical 

height when the term (tan β – tan ϕ’) becomes negative. This happens when the slope angle is 

less than the angle of internal friction. In this case, the negative value has to be considered as 

an infinite critical depth, because if the slope angle is less than the angle of internal friction, 

failure is unlikely to occur. 



Figure 4. 4 Critical depth to lower boundary under fully saturated condition 

The critical depth under fully saturated condition ranges from 0 to 20 m. Around 31% of the 

area has a slope angle smaller than the angle of internal friction resulting in a stable condition. 

An additional 22% of the area is characterized by negligible soil cohesion; hence, its stability 

is independent of depth of material above the failure plane. The depth of the slide plane is 

only less than 1.5 m for 16% of the area. A material thickness of less than 2.5 m shall be 

maintained over 27% of the area to attain stability. 41% of the area should have a depth to the 

lower boundary of less than 10 m under fully saturated condition. Figure 4.5 summarizes the 

areal distribution versus critical depth. 

4.1.3.2 Half saturated critical depth 

The critical height map under half saturated scenario is shown in Figure 4.5(a). 22% of the 

area is characterized by negligible cohesion, and hence its stability is independent of depth. 

56% of the area has a slope angle small enough to have a sufficient frictional resistance 

against shear failure. This Figure was 31% for fully saturated scenario. The larger the amount 

of water in the regolith, the lower will be the frictional resistance between particles and the 

more unstable the slope will become. In addition, a soil depth of less than 3.5m and 10m shall 

be maintained over 15% and 20% of the area, respectively, to prevent instability. 



4.1.3.3 Dry critical depth 

The dry scenario gives the most stable condition. 68% of the slope angles are small enough to have a sufficient frictional resistance against shear 

failure. 7.5% of the area shall have a depth to the failure plane of less than 10m to prevent instability. 

Figure 4.5 Critical depth to lower boundary, (a) under half saturated, (b) under dry conditions 



Figure 4.6 Areal distribution of critical depths under saturated, half sat and dry scenarios 

During dry condition, around 68% of the area has a slope angle sufficiently lower than the 

internal angle of friction to control instability. However, this decreases to 56% and 31% for 

half saturated and saturated scenarios. This is explained by the fact that the frictional 

resistance between particles is reduced due to the presence of water. Therefore, it can be said 

that slope angles that were significantly smaller than the internal angle of friction to control 

sliding would become susceptible with the increase in soil wetness. 

4.1.4 Factor of safety calculation 

4.1.4.1 Completely dry condition 

The stability of a slope under completely dry condition is governed by cohesion, angle of 

internal friction, slope angle and the depth of the material above the bedding plane. Since, 

there is no pore water pressure developed, this scenario gives the highest amount of area 

under stable condition. However, due to very steep slope and poor material strength in shear, 

the minimum safety factor for this situation is observed to be 0.03. In some flat parts of the 

study area the safety factor is observed to be over 4000. Under completely dry scenario, 



80.3% of the area is found to be stable, 8.4% is regarded as moderately stable, 6.4% and 4.9% 

are found to be quasi stable and unstable respectively. 

The safety factor map displaying the inventoried landslides and rocky cliffs is shown in 

Figure 4.7. 

Figure 4.7 Classified factor of safety map under dry condition 

The next step is to check how many of the inventoried landslides are indeed identified by the 

infinite slope model. For this, a cross operation is performed between the categorized factor of 

safety map under dry condition and the landslide inventory using ILWIS-GIS. The result is 

shown in the Table 4.2. 

Table 4.2 Model validation under dry condition 

Stability class 

Observed landslides 

(# of pixels) % of LS LS susceptibility class 

Unstable 965 19.02 Very high 

Quasi stable 987 19.46 High 

Moderately stable 944 18.61 Moderate 

Stable 2177 42.91 Low/Very low 



Under completely dry condition, 38% of the observed landslides are identified in high and 

very high landslide susceptibility classes. An additional 19% of the failure units are in zones 

of moderate hazard. According to the infinite slope model, however, 43% of the landslides 

will not be mobilized, if the soil remains dry. 

To investigate the stability condition with slope classes, a cross operation between slope 

categories and factor of safety classes is carried out. The result is shown in Table 4.3. 

Table 4.3 Stability vs slope classes 

Stability condition 

Flat Rolling Mountainous Escarpments Cliffs Remark 

(%) (%) (%) (%) (%) 

Unstable 0.00 0.01 8.83 57.40 81.82 Very high LS susc. 

Quasi stable 0.00 0.00 26.88 33.33 5.45 High LS susc. 

Moderately stable 0.00 3.22 35.11 9.26 12.73 Moderate LS susc. 

Stable 100.00 96.77 29.18 0.00 0.00 Low/Very low LS susc. 

In flat and rolling terrains, the slope is unlikely to fail under completely dry condition. 37% of 

areas with slope angle 15°-25° are regarded as high and very high landslide susceptibility 

zones. 91% of the escarpments in the study area, with slope angle ranging from 25° to 45°, are 

likely to fail without the action of water. However, this Figure lowers to 87% for larger slope 

angles i.e. for cliffs (with slope angle between 45° to 60°). This is explained by the nature of 

the formations. If it weren’t for the hard and firm composition of such cliffs, a profile slope of 

over 45° would have never been attained. Therefore, such formations have large strength in 

shear that decreases the extent by which sliding occurs. 

The stability condition against geologic formations is assessed in Table 4.4. 

Table 4.4 Stability vs geologic formations 


Trap 

basalt 

Limestone 

Marl 

Amba 

Aradom 

Sandstone 

Shale Dolerite 

Sill 

Shale Marl 

Limestone 

Marl 

Limestone 

Endaga 

Adigrat 

Sandstone Alluvium Arbi 

Glacial 

Entcho 


(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 

Unstable 0.02 2.49 16.19 3.41 0.91 1.23 0.00 32.61 0.00 2.52 11.88 

Quasi stable 5.80 7.03 16.85 6.68 4.85 5.73 2.22 13.71 0.00 0.00 4.99 

Moderately stable 10.84 9.17 11.06 9.02 4.21 6.86 7.48 9.10 0.00 0.00 3.66 

Stable 83.34 81.31 55.89 80.89 90.04 86.18 90.29 44.57 100.00 97.48 79.47 

33% of Amba Aradom sandstone formation is likely to fail under completely dry scenario. 

46% of Adigrat sandstone is characterized by a factor of safety less than 1.25. Adigrat 

sandstone is a hard formation, however the slope angles of part of such cliffs (located around 



NW of the study area) is sufficiently large that mohr-coloumb criteria is not satisfied. 10% of 

shale, 10% of Limestone marl and 6% of trap basalts are located in high and very high 

landslide susceptibility classes, even if the soil is kept dry. Failure in alluvium, Endaga Arbi 

glacial, dolerite sill and marl limestone is unlikely to occur in this situation. 

4.1.4.2 Half saturated condition 

The stability of a slope under half saturated condition is controlled by cohesion, angle of 

internal friction, slope angle, the thickness of the material above the bedding plane and pore 

water pressure. Half the thickness of the material is assumed to be saturated with water. 

21.67% of the area is found to be unstable, 12.85% is quasi stable while moderately stable and 

stable classes constitute 13.02% and 52.46% respectively. Therefore, under half saturated 

scenario, 34% of the study area is defined to be high and very high landslide susceptibility 

category. This value exceeds the area identified by the landslide inventory by 14%. 

The safety factor map overlaid by the inventoried landslides and rocky cliffs is shown in 

Figure 4.8. From Figure 4.8, it is observed that, infinite slope model identifies most of the 

rock falls under half saturated scenario. For landslide bodies, the depletion areas are 

recognized to be quasi stable and unstable (FS < 1.25 and FS


Figure 4.8 Classified factor of safety map for half saturated condition 

Table 4.5 Model validation under half saturated condition 


Observed 

landslides 


Unstable 3145 61.99 Very high 

Quasi stable 633 12.48 High 

Moderately stable 430 8.48 Moderate 

Stable 865 17.05 Low/Very 

Under half saturated scenario, 74% of the observed landslides are identified in high and very 

high landslide susceptibility classes. An additional 9% of the failure units are in zones of 

moderate hazard. In the study area, if slope evaluation is made using this model under semi 

saturated condition, 17% of failure zones won’t be recognized. However, this is not a bad 

Figure due to the following reasons: 



Part of those unrecognized landslides could have been triggered by a rainfall incidence 

which might have saturated more than half the thickness of the material. 

In compiling the landslide database, not all the erosional belts of the mass movements 

could be identified accurately. This is mainly due to the fact that only morphology of 

depositional bodies are clearly visible using aerial photographs, and the depletion 

zones had more difficult morphologies to be delineated accurately. So, there will be 

some pixels of depositional lobes in the database, which will turn out to be stable by 

the infinite slope model. 

To correlate the stability condition with slope classes, a cross operation between slope 

categories and factor of safety map is carried out. The result is shown in Table 4.6. 

Table 4.6 Stability vs slope classes under semi saturated condition 

Slope catagories 

Stability condition Flat Rolling Mountainous Escarpments Cliffs Remark 

(%) (%) (%) (%) (%) 





In flat terrain, the slope is unlikely to fail under half saturated condition. Unlike the dry 

condition, instability starts to happen on slopes > 5°. Only 54% of the rolling terrain is 

assured to have low landslide susceptibility. An additional 62% to the 37% of the 

mountainous areas that were defined to likely fail in dry condition will be regarded as zones 

of high and very high landslide susceptibility classes. If half of the material depth is assumed 

to be saturated with water, all areas with slope angle 25-45° will possibly fail. The same is 

true for cliffs. The stability condition against geologic formations is looked into table 4.7. 

Table 4.7 Stability vs geologic formations under semi saturated scenario 

Amba 

Endaga 


Limestone Aradom Dolerite Shale Marl Marl Adigrat 

Arbi Entcho 

Trap basalt Marl Sandstone Shale Sill Limestone Limestone Sandstone Alluvium Glacial Sandstone 

(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 

Unstable 24.46 16.44 43.79 21.83 7.88 13.44 11.58 55.20 0.00 2.52 20.25 

Quasi stable 14.73 17.18 13.67 17.30 6.87 10.74 12.74 10.24 0.00 0.00 4.85 


Stable 46.99 51.58 29.23 43.03 77.04 61.89 62.51 26.36 94.52 96.89 69.97 



57% of Amba Aradom sandstone formation is likely to fail under half saturated scenario. 65% 

of Adigrat sandstone is characterized by a factor of safety less than 1.25. Adigrat sandstone is 

a hard formation, however the slope angles of part of such cliffs (located around NW of the 

study area) is sufficiently large that mohr-coloumb criteria is not satisfied. Saturating half of 

the material depth is enough to mobilize an additional 33% of trap basalts than the 6% 

identified during the dry condition. 39% of shale and 34% of Limestone marl are recognized 

to be in high and very high landslide susceptibility zones. It is observed that the highest effect 

of the developed pore-water pressure is felt in the basalts. This is explained by the nature of 

expansive clays resulting from the weathering of basalts. Moreover, failure in alluvium and 

Endaga Arbi glacial is unlikely to occur in this situation. 

4.1.4.3 Full saturated condition 

The stability of a slope under fully saturated condition is controlled by cohesion, angle of 

internal friction, slope angle, depth to solid rock and pore water pressure. The ground water 

table is assumed to be parallel with the bedding plane and coincide with the ground surface. 

This scenario gives the worst instability that could exist owing to the considered parameters. 

The factor of safety map is shown in Figure 4.9. 39.34% of the area is characterized by a 

factor of safety of less than one, 13.65% is quasi stable while moderately stable and stable 

classes contribute to be 12.39% and 34.62% respectively. Hence, considering the parameters 

involved in this study, the worst situation that could happen is that 53% of the area will be 

under high and very high landslide susceptibility zones. This is comparably very large as 

compared with the landslide inventory which puts 20% of the area in landslide zone. In 

comparison with the inventory, fully saturated scenario assigns an additional 1/3 of the study 

area i.e. 164km² under risk zone. If extreme rainfall situation permits full saturation, only 35% 

of the area will be resistant in shear. 

From Figure 4.9, it is observed that, infinite slope model identifies almost all rock falls under 

fully saturated scenario. The landslide lobes are also significantly recognized; especially the 

depletion units are defined to have a FS < 1. Parts of the depositional lobes could reasonably 

have a FS > 1, as these areas are not the place where the landslides get mobilized. 



To depict the extent by which the final output under full saturated scenario conform to the 

landslide database, Table 4.8 shows a cross validation between the categorized factor of safety 

map and the landslide inventory. 

Figure 4.9 Classified factor of safety map under full saturated condition 

Table 4.8 Model validation under fully saturated condition 


Observed 

landslides 


Unstable 4020 79.24 Very high LS susc. 

Quasi stable 369 7.27 High LS susc. 

Moderately stable 249 4.91 Moderate LS susc. 

Stable 435 8.57 Low/Very low LS susc. 

An infinite slope model based on completely saturated scenario identifies 86% of the 

observed landslides in high and very high landslide susceptibility classes. An additional 5% of 

the failure units are defined in zones of moderate hazard. On the study area, if slope stability 



analysis is performed using this model under full saturated condition, only 9% of actual 

failure zones wouldn’t be recognized. 

The stability condition is assessed with slope classes in Table 4.9. 

Table 4.9 Stability versus slope classes 

Slope catagories 

Stability condition Flat Rolling Mountainous Escarpments Cliffs Remark 

(%) (%) (%) (%) (%) 





In flat terrain, the slope is unlikely to fail under full saturated condition. However unlike the 

other scenarios 1.12% of slopes < 5° are observed to have a FS < 1.5. Only 20% of the rolling 

terrain is assured to have low landslide susceptibility, this Figure amounts to 10% of the study 

area. 56% of the area with slope angle between 5° to 15° is characterized by a FS < 1.25. If 

the weathered material is fully saturated, failure is eminent in slopes > 15°. The shear strength 

of 100% of areas with slope angles between 15° to 45° will be fully mobilized, and hence 

failure will likely occur. The same is true for cliffs. It could be deduced from the above table 

and areal distribution of the terrain classes that if the weathering products are allowed to fully 

saturate, only 35% of the study area will be stable. Therefore such a condition shall be 

considered for the long term estimation of stability. However, for short term modeling, 

assumption of full saturation is not realistic. 

The stability condition is assessed with the lithology in Table 4.10. 


Amba 

Aradom 


Endaga 

Arbi 

Glacial 


Limestone 

Dolerite Shale Marl Marl Adigrat 

Entcho 

Trap basalt Marl 

Shale Sill Limestone Limestone Sandstone Alluvium Sandstone 

(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 

Unstable 50.69 34.17 60.88 50.87 12.57 26.49 26.03 68.22 0.00 2.61 26.09 

Quasi stable 14.39 15.27 14.04 16.30 8.36 15.77 14.53 7.61 1.37 1.09 5.91 


Stable 23.71 37.52 14.02 21.66 70.80 41.46 43.50 18.18 80.82 92.94 61.88 

74% of Amba Aradom sandstone formation is likely to fail under this scenario. Fully 

saturation of the soil develops a pore pressure that could mobilize an additional 59% of trap 



basalts than the 6% that was identified during the dry condition. 75% of Adigrat sandstone is 

characterized by a factor of safety less than 1.25. 67% of shale, 49% of Limestone marl, and 

42% shale –marl limestone will have a factor of safety less than 1.25. It is observed that the 

highest effect of the developed pore-water pressure is felt in the basalts. Swelling clays are the 

weathering products of basalts in the study area. These clay minerals could increase twice in 

volume upon the entrance of water. The impact of water is two sided for slope stability; (1) it 

breaks the natural anchorage between the soil grains and decreases the strength the soil has in 

shear (2) it creates an uplift pressure which counterbalances the vertical component of the 

weight. Owing to the higher areal distribution covered by trap basalt in the study area, 

emphasis shall be given to instabilities occurring in the formation. 

4.1.4.5 Stability calculation by combining three steady state scenarios, dry, 

half saturated and fully saturated. 

Now, we will investigate the stability analysis by combining the three steady state scenarios, 

using the classification shown in Table 4.11. 

Table 4.11 Combined factor of safety classification 

Category 

Factor of safety criteria 

Saturated Half sat. Dry 

Unconditionally stable FS > 1.5 Stable in completely saturated condition 

Stable 1.25 < FS < 1.5 

Moderately stable 1 < FS < 1.25 

Criteria 

Moderately stable in completely saturated 

condition 

Quasi stable in completely saturated 

condition 

Moderately unstable FS < 1 FS > 1 FS > 1 

Unstable in completely saturated condition 

but not in half saturated and dry conditions 

Unstable FS < 1 

Unstable in half saturated condition but not 

FS > 1 

unstable in dry condition 

Unconditionally unstable 

FS < 1 unstable in dry condition 



Figure 4.10 Stability map based on three steady state scenarios 

40 

Percentage of area 

30 

20 

10 

0 

Unconditionally stable 

Stable 

Moderately stable 

Moderately unstable 

Unstable 

Unconditionally unstable 


Figure 4.11 Areal distribution of stability classes under three steady state scenarios 



Figure 4.10 and 4.11 show the combined hazard map and areal distributions under the three 

steady state scenarios. Based on an infinite slope model and owing to the considered 

parameters, 34.6% of the study area is unconditionally stable, and will not fail under any 

circumstances unless major destabilizing activities occur. If extreme rainfall event leads to 

full soil saturation, 12.4% of the area will face instability under moderate destabilizing 

actions, 13.7% of the area will likely fail under minor destabilizing forces and 17.7% of the 

area needs stabilization methods to control instability. Landslides will be mobilized in about 

16.7% of the area, if the soil is half saturated. 4.93% of the area will likely fail under any 

condition, and hence stabilization techniques are recommended. 

We generalized from the three steady state scenarios that 21% of the area is under high and 

very high landslide susceptibility zone. An additional 17.67% of the area is categorized under 

instability zone with a moderate hazard. The result is quite coherent with the 20% that was 

identified with the landslide inventory. 

To verify the model output, a cross operation is performed against the landslide database. The 

result is summarized in Table 4.12. 

Table 4.12 Validation of the model (a) with the whole database (b) with validation group 

Stability class Observed landslides % of LS Stability class Observed landslides % of LS 

(# of pixels) 

(# of pixels) 

Unconditionally stable 435 8.57 Unconditionally stable 156.6 8.31 

Stable 249 4.91 Stable 104.58 5.55 

Moderately stable 369 7.27 Moderately stable 171 9.08 

Moderately unstable 875 17.25 Moderately unstable 360 19.11 

Unstable 2180 42.97 Unstable 764 40.56 

Unconditionally unstable 965 19.02 Unconditionally unstable 327.4 17.38 

On the study area, if slope stability analysis is performed using infinite slope model, 79% any 

landslide incidences will be identified in high and very high landslide susceptibility zones. An 

additional 7% of the failure zones might be regarded to be initiated in long term condition. 

However, the model fails to recognize 14% of mobilized landslides under normal 

circumstances. Yet, the output obtained is still appreciable provided the following reasons: 

While compiling a landslide database, not all landslide depletion areas could be 

accurately identified. The reason is the morphology of some erosional belts is difficult 

to be interpreted by aerial photographs. On the other hand, depositional lobes could be 



clearly delineated. Therefore, in some instances, the landslide inventory is expected to 

contain some depositional areas which will turn out to be stable in the model. 

The input data are obtained in different formats. The analysis is performed using three 

different softwares. Some data manipulation would be performed well using one 

software while other data format will not. So, the analysis is conjugated with data 

transformations and arrangements. Moreover, the landslide data base is created using 

aerial photo interpretation and manual delineation on the topographic map using GIS. 

It is clear that allowable projection and consistency errors will be included. 

On the coming chapter, we will be dealing with statistical models. As these methods solely 

depend on the inventory, the landslide data base will be divided in to two. (1) training mass 

movement bodies that will be used to built the model (2) validation landslide bodies which 

will be used to check the accuracies. To compare infinite slope model with the statistical 

methods, we shall confirm it with the validation group. 

Table 4.12(b) shows the cross-checking with validation landslides. The accuracy obtained in 

this case is 77% which is almost identical with the one found for the whole data base. 

Next step is assessing the combined stability against slope classes, 

Percentage 

100 

90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

Flat Rolling Mountainous Escarpments Cliffs 

Unconditionally 

stable 

Stable 

Moderately stable 

Moderately unstable 

Unstable 

Unconditionally 

unstable 

Figure 4.12 Stability under three steady state scenarios vs slope classes 



Failure is unlikely to occur in any normal circumstance on slopes < 5°. 31% of the area with 

slope angle 5°-15° is unstable or moderately unstable, while 20% of it is unconditionally 

stable. 9% of mountainous terrain needs stabilization techniques, while 72% of it would likely 

fail under minor destabilizing forces when the soil is kept saturated. 57% of the escarpment 

and 82% of cliffs need stabilization approaches to mitigate slope failures. Well, the results 

prove that slopes > 15° are favorable for instability in the study area. 

The same assessment is done for geologic layers in Table 4.17. 


Stability classes 

Trap Limestone 

basalt Marl 

Amba 

Aradom 


Shale Dolerite 

Sill 

Shale Marl 

Limestone 

Marl 

Limestone 

Endaga 

Adigrat 

Sandstone Alluvium Arbi 

Glacial 

Entcho 


(%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 

Unconditionally stable 23.71 37.52 14.02 21.66 70.80 41.46 43.50 18.18 80.82 92.94 61.88 

Stable 11.21 13.04 11.06 11.16 8.26 16.27 15.93 5.99 17.81 3.36 6.12 


Moderately unstable 26.23 17.73 17.08 29.04 4.69 13.05 14.45 13.03 0.00 0.08 5.84 

Unstable 24.44 13.95 27.60 18.42 6.98 12.21 11.58 22.58 0.00 0.00 8.37 

Unconditionally unstable 0.02 2.49 16.19 3.41 0.91 1.23 0.00 32.61 0.00 2.52 11.88 

32.41% of Adigrat sandstone cliffs near NW of the study area will not satisfy mohr-coloumb 

criteria under any conditions. 27.6% of Amba Aradom sandstone and 24.44% of trap basalts 

are found to be unstable. 29% of shale and 26% of trap basalts are moderately unstable. Trap 

basalts contribute the highest areal coverage in the study area as compared with other 

formations. In addition, it is likely that the local people tend to settle on the basalts for search 

of fertile lands. Therefore, it is concluded that the highest instability risk that would be 

considered is on trap basalt formation. 


Chapter 5: Statistical Analysis approach 

5.1 General 

Statistical analysis of slope stability is often indirect. It depends mainly upon a relationship 

between past landslides and causative factors. The first most important step is identifying 

factors whose combinations have led to past failures. This requires a precise observation on 

how the failures are triggered. Understanding the failure mechanisms helps in weighting the 

factors beforehand. Quantitative prediction of instability is then made on areas that are free of 

landslides but with similar characteristics, with respect to factors inducing landslides. The 

second step is recognizing the extent by which different combinations of the factors yield 

unstable conditions. In our study, we investigated 10 causative factors: slope, lithology, 

elevation, landuse, distance to faults, distance to streams, slope shape, aspect, distance to 

roads, and topographic wetness index. Multiple perspectives of combining the factors are 

investigated. The worst scenario with the highest accuracy is then selected. We employed 

three combinations: 

1: Considering all the 10 factors. 

2: Considering the pre-estimated most governing and those which are not related to each other 

i.e. slope, lithology, distance to faults and landuse. 

3: Considering factors which prove to show a discernible causative relationship with 

landslides in the study area, i.e. slope, lithology, landuse, aspect and slope shape. 

Statistical approaches are widely employed in hazard assessment studies. The prominent 

features of these methods are high efficiency, low cost, better and precise understanding 

between the spatial factors and instability. Moreover, inaccurate spatial representations of soil 

parameters and failure plane assumptions are removed. However, the methods lack 

consideration of landslides that exist in areas which are not inventoried. Aerial photograph 

interpretation might identify those landslides, but it still depends on the expertise of the 

investigator to recognize small scale morphologies. A reliable approach is combining field 

investigations and aerial photograph interpretation in building the landslide database. 


Chapter 5: Statistical models 61 

5.2 Bivariate statistical analysis 

The basic idea of bivariate statistical analysis is calculating the densities of landslide 

occurrences for each causative factor and within its categories. The calculation is used to 

drive data driven weights for each factor to contribute to failure. Different combinations of the 

weights are applied to obtain a landslide hazard map. In order to apply bivariate analyses, 

continuous parameter maps have to be converted into discrete (categorical) maps to compute 

the corresponding weight for each class. However, such conversions remain always unclear as 

most authors use their expert opinion in dividing the classes (Van Westen, 1993; Soeters and 

Van Westen, 1996). Hence, it is reasonable to argue that the classification of the factors is 

rather subjective. But multiple trials of categorization can be considered until a reasonable 

causative relationship is attained. 

For a bivariate statistical approach, maps of medium scale are most appropriate, in the range 

of 1:25000 to 1:500000 (Van Westen et al., 1997), because the technique is not detailed 

enough to be applied on a larger scale. In bivariate statistical analysis, the occurrence of mass 

movements is related to each causative factor independently and the weights of the parameters 

are calculated separately. The method relies on the assumption that the relative importance of 

the inducing factors is identified by calculating the density of mass movements for each 

variable class. Integrating various parameter maps into a new map and cross checking with 

the landslide data base gives densities per unique combinations. 

5.2.1 Statistical index method 

The Statistical index method is a bivariate statistical method introduced by Van Westen, 

(1997) for landslide susceptibility analysis. A weight value for each categorical unit is defined 

as the natural logarithm of the landslide density in the categorical unit divided by the landslide 

density in the entire map. This method is based on the following formula (Van Westen, 1997): 

Where, 

∗ 

A 

W = ln f 

f = ln ⎛ A 

⎞ ⎛ A ∗ 

 

⎜ A 

∗⎟ 

= ln A 

⎜ 

∗ ⎞ 

 

A 

⎟ 

A 

A 

⎝ ⎠ ⎝ ⎠ 

W ij = weight given to a certain class i of parameter j; 

f ij = landslide density within class i of parameter j; 

(5.1) 



f = landslide density within the entire map; 

A ij * = Area of landslides in a certain class i of parameter j; 

A ij = Area of a certain class i of parameter j; 

A* = Total area of landslides in the entire map; 

A = Total area of the entire map. 

The weight value for each class is only calculated for those that have landslide observations, 

either from field or aerial photograph interpretation. For those instances when no landslide is 

identified, the weight will be assigned to zero (Van Westen, 1997). This means that a 

parameter class with no landslide incidence will have no sufficient evidence in relation to 

future landslide occurrence. Hence, its influence on the calculation of the landslide 

susceptibility index will be zero. In this study, every parameter map is crossed with the 

landslide map, and the density of the landslides in each class is calculated. The distribution of 

landslides for various data layers and weight values are performed with a script written using 

ILWIS – GIS. The results are shown in Table 5.1. 

We note from the calculation of the weights that: 

For slope classes there exists a clear causal relationship between steeper slope angles 

and landslides. With increasing order of correlation, mountainous terrain, escarpments, 

and cliffs show a positive relationship with mass movements. Flat and rolling terrains 

prove to be unfavorable for landslides. Thus, it is concluded that landslides occur in 

the study area in areas with slope angle > 15°. 

Alluvium, Dolerite sill, Amba Aradom sandstone, Trap basalts and marl limestone 

prove to have properties favorable for instability. The highest area of landslides is 

observed for Trap basalts and marl limestone. This observation is quite coherent with 

the landslide mechanisms explained in chapter 3. The positive correlation attributed to 

Adigrat sandstone arises from cliff rock fall. However, Agula shale has a negative 

weight value. This contradicts with failure mechanism B. The possible reason for this 

is the wide area covered by the formation which lowers its landslide density. 

According to failure mechanism B, there are landslides mobilized in shale but the flow 

is also ascribed with landslides mobilized in Antalo limestone. Considering the 

positive correlation of Marl limestone, Amba Aradom sandstone, Dolerite sill, and 

trap basalts with instability, we conclude that the three governing landslide 

mechanisms are well detected with the statistical index method. 



Table 5.1 Weight assignment, statistical index method 

Area of 

Area of 

Class 

Area of observed Weight 

Area of observed Weight 

Class 

category landslides 

category landslides 

(# of pixels) (# of pixels) (# of pixels) (# of pixels) 

Slope 

Flat 13448 1367 -0.73 

slope shape 

Peak and Ridges 11267 2156 -0.07 

Rolling 31512 5725 -0.15 Saddle 15745 3380 0.04 

Mountanious 10664 3289 0.38 Slope Hill 25 5 -0.03 

Escarpments 2979 1912 1.12 Convex 8738 1947 0.08 

Cliffs 61 51 1.38 Concave 8774 1973 0.09 

Total 58664 12344 Gully 13542 2595 -0.07 

Lithology 

Other 2021 288 -0.37 

Trap basalt 12766 3210 0.20 Total 60112 12344 

Limestone Marl 5291 611 -0.58 Distance to streams 

Amba Aradom Sandstone 3489 1340 0.63 Very Close 7066 1381 -0.05 

Shale 6019 891 -0.33 Close 12703 2592 -0.01 

Dolerite Sill 1877 942 0.89 Moderate 11028 2321 0.02 

Shale Marl Limestone 10650 1102 -0.69 Fairly Moderate 12883 2784 0.05 

Marl Limestone 12093 2888 0.15 Far 12061 2534 0.02 

Adigrat Sandstone 5243 1292 0.18 Very far 4371 732 -0.20 

Alluvium 73 49 1.18 Total 60112 12344 

Endaga Arbi Glacial 1189 0 0.00 Distance to roads 

Entcho Sandstone 1422 19 -2.73 Close 3234 563 -0.17 

Total 60112 12344 Medium 2902 503 -0.17 

Elevation 

Fairly Far 2661 562 0.03 

Low 729 0 0.00 Very Far 51315 10716 0.02 

Moderate 2842 0 0.00 Total 60112 12344 

Fairly Moderate 17438 2358 -0.42 Aspect 

High 33827 8351 0.18 Flat 14 0 0.00 

Very High 5276 1634 0.41 N 4686 1118 0.15 

Total 60112 12343 NE 8815 1728 -0.05 

Landuse 

E 8147 1052 -0.46 

Bare Soils 39356 8844 0.09 SE 7097 1354 -0.07 

Sandy/Whitish Clay 3881 521 -0.43 S 8507 1612 -0.08 

Urban 0 0 0.00 SW 6647 1092 -0.22 

Vegtation 16563 2919 -0.15 W 6433 1540 0.15 

Total 59800 12284 NW 6263 1863 0.37 

Distance to faults 

N2 3436 983 0.33 

Very Close 15140 2599 -0.18 Total 60045 12342 

Close 12576 2480 -0.04 Wetness index 

Moderate 7851 1613 0.00 WI


fairly moderate elevation proves to be unfavorable, while high and very high elevation 

classes have positive correlations with mass movement. 

Concave slope shapes prove to be relatively highly correlated with landslides than 

other shapes. Convex, and saddle shapes also yield small positive weights 

For distance to streams, fairly moderate class yields the highest matching index. 

However, all weights observed for distance to stream factor are close to zero. This 

means that there is not sufficient evidence to demonstrate a significant positive or 

negative relationship between distance to streams and landslides. 

For distance to roads, our results depict an unclear causal association between this 

factor and instability. 

North, Northwest and West facing slopes prove to be positively related with mass 

movements in a decreasing order. On the other hand, those slopes which face opposite 

to the formers, East, Southwest, and South, turn out to be most unfavorable to 


Bare soils are observed to be highly affected by landslides. Moreover, the highest area 

of observed landslides is in this category. While vegetation, as expected, proves to 

have a negative relationship with landslides. 

For distance to fault, unlike to what was expected, the highest positive matching is 

observed for moderately far and far distances to faults, and the lowest correlation 

occur in very close and close categories. As distance to faults is one of the important 

factors inducing landslides, this seems eccentric. First, to investigate if the discrepancy 

happens to be from the factor employed, another consideration is checked i.e. using 

fault density. The fault density is calculated to be the total length of fault over a 

random square unit of 300m x 300m. Another zoning is also tried for total fault length 

over each lithological unit. The results in both cases remain to be the same as to what 

we found for distance to faults. Therefore, the explanation is as follows: faults and 

fractures affect the stability of earthen slopes by destroying the anchorage between 

soil grains. This decreases the shear strength of the soil. However, failure will only 

occur provided the mobilized shear stress is greater than the shear strength. If the 

intensity of fracturing is not strong enough to reduce the strength below the stress, 

failure will not occur. In addition, if the faulting activity has ceased then the shear 

strength is not expected to decrease any further. Also, there will be no effect of 

faulting, if the direction it occurs is opposite to the failure plane. Most of the very high 



fault density zones exist in Adigrat sandstone formation which has a large shear 

strength. They also exist on flat and rolling terrains with quite low slope angles, which 

yield a small value for the mobilized shear stress. Hence, we conclude that though 

faulting is an important inducing factor for destabilizing slopes, it is not observed to 

significantly destabilize slopes in Hagere Selam. 

For the topographic wetness index, those localities with a TWI < 2.5 prove to be 

significantly affected by landslides. 

The weights of the causative factors are summed to obtain a landslide susceptibility map: 

Where: 

LSI: landslide susceptibility index 

 

LSI = W 

 

(5.2) 

W ij : weight of class i in parameter j 

n: number of causative factors combined 

5.2.1.1 Considering all factors (SI model 1) 

The landslide susceptibility index, LSI, is established in ILWIS software using formula 5.2. 

The minimum value obtained for the entire map is -5.28 while the maximum is 2.946. The 

Mean value is -0.58 and standard deviation is 1.67. To finalize the hazard zonation map, the 

LSI map is classified on the basis of the relationship between LSI values and observed 

landslides. Usually, the LSI frequency diagram and landslide occurrences are matched and 

used for the landslide hazard zonation. However, this is not straight forward as there are no 

statistical rules which can categorize continuous data automatically (Ayalew et al., 2004; 

Long, 2008). The problems of transforming a continuous data set into a categorical unit seem 

to be unclear, as most researchers employ subjective approaches. In this study, we adopted the 

manual classifier method to classify the LSI values into five different susceptibility zones. 

The landslide susceptibility classes are: very low, low, moderate, high and very high. 

The landslide database is divided in to two groups. The first group contains training pixels 

that are to be employed for classifying the LSI map and the second group containing 

validation pixels. The proportion of training and validation pixels is considered to be 60% and 

40% respectively. 



Normally, the classification procedure shall satisfy the principle that higher landslide 

susceptibility classes should indicate more landslide events than lower landslide susceptibility 

classes. Therefore, it is assumed that the expected number of training pixels within a higher 

landslide susceptibility class equals two times than that of the expected numbers in the next 

lower landslide susceptibility class (Long, 2008). For instance, the expected number of 

training landslide pixels in the low landslide susceptibility class should equal two times the 

expected numbers in the very low landslide susceptibility class, and so on. Thus, it can be 

shown that, if X is the percentage of training landslide pixels in the very low landslide hazard 

class: 

31X = 100%, ⤇ X = 3.225% 

Similarly, the percentages of training landslide pixels in low, moderate, high and very high 

classes would be 6.45%, 12.9%, 25.8% and 51.6% respectively. 

The landslide occurrence map is compared to the LSI values, and the cumulative percentage 

of training landslide pixels against its LSI values is arranged in an ascending order. Four 

cutoff percentages of the cumulative percentage are used to identify the five landslide hazard 

zones. The cutoff values are 3.225% for separating very low to low, 9.65% for separating the 

low from the moderate class, 22.55% for separating the moderate from high class, and finally 

48.35% for separating the high from the very high class. At the cutoff points, the 

corresponding LSI cutoff values are determined, and the map of landslide susceptibility 

zonation is created from the LSI map using the respective LSI cutoff values. The procedure is 

shown in Figure 5.1. 



Figure 5.1 Percentage of observed training landslides versus LSI (SI model 1) 

The cutoff landslide statistical index values are: 

‣ Very low landslide susceptibility: LSI < -1.30 

‣ Low landslide susceptibility: -1.30 ≤ LSI < -0.69 

‣ Moderate landslide susceptibility: -0.69 ≤ LSI < -0.15 

‣ High landslide susceptibility: -0.15 ≤ LSI < 0.45 

‣ Very high landslide susceptibility: LSI ≥ 0.46 

The final hazard map and the areal distribution of each susceptibility classes are shown in 

Figure 5.2 and Figure 5.3 respectively. It is observed that very low, low, moderate, high and 

very high susceptibility classes represent, 17%, 24%, 26%, 14% and 19% of the study area. 

The very high and high hazard classes are considered to be representative for future landslide. 

While the total area of landslides inventoried was about 20% of the study area, the total area 

of high and very high susceptibility classes comprise about 33%. 



Figure 5.2 Landslide hazard map (SI model 1) 

30 

25 

20 

15 

10 

Area (%) 

5 

0 

Very low LSS Low LSS Moderate LSS High LSS Very high LSS 

Figure 5.3 Areal distribution of susceptibility classes (SI model 1) 



To validate the final output, the hazard map is cross checked with validation landslide bodies. 

To perform this, a cross operation is done between the validation landslide pixels and the 

categorized susceptibility map, using ILWIS GIS. The result is shown in the Table 4.15. 

Table 5.2 Validation of SI model 1 

Landslide susceptibility Area of validation landslides (pix) Percentage 

Very low 24 1.20 

Low 94 4.63 

Moderate 413 20.37 

High 362 17.83 

Very high 1135 55.97 

Total 2028.40 100.00 

Hence, considering all the 10 causative factors as equally important to failure and future 

landsliding, the statistical index model 1 identifies the landslide activities in the study area 

with 74% accuracy, as it assigns 56% and 18% of actual failure zones to the very high and 

high susceptibility classes. 20% of the landslide incidences are in the moderate hazard zone 

and 5.63% in the low and very low risk zones. 

5.2.1.2 Considering the pre-estimated most governing factors (SI model 2) 

Slope, lithology, distance to faults and landuse are pre-estimated to be most governing as 

compared to the other factors. In addition, those factors are not related to each other. Hence a 

LSI map is created using only those factors. The minimum value of LSI obtained is -3.54 and 

the maximum is 2.29. The mean value is -0.19 and the standard deviation is 1.25. The LSI 

map is classified on the basis of the relationship between LSI values and training landslide 

bodies, as explained before. 

The cutoff landslide statistical index values are: 



‣ Moderate landslide susceptibility: -0.84 ≤ LSI < 0.16 

‣ High landslide susceptibility: 0.16 ≤ LSI < 0.42 




Figure 5.4 Percentage of observed training landslides versus LSI (SI model 2) 

The final hazard map and the areal distribution of each susceptibility classes are shown in 

Figure 5.5 and 5.6 respectively. It is observed that very low, low, moderate, high and very 

high susceptibility categories constitute 25%, 12%, 32%, 12% and 19% of the study area. The 

total area of landslides inventoried was about 20% of the study area. The statistical index 

method of analysis, model 2, predicts that 31% of the area is high or very high landslide 

susceptibility class. Compared with model 1, this is only 2% less. 




35 

30 

25 

20 

15 

10 

Area (%) 

5 

0 

Very low ls. s. Low ls. s. Moderate ls. s. High ls. s. Very high ls. s. 




To validate the final output, we made a cross operation between the validation landslides 

pixels and the categorized hazard map of SI model 2. The result is shown in the Table 5.3. 

Table 5.3 Validation of SI model 2 

Landslide susceptibility Area of validation landslides (pix) Percentage 


Low 137 6.74 


High 300 14.78 

Very high 1083 53.36 

Total 2029 100.00 

Considering only slope, lithology, distance to faults and landuse as factors equally important 

to failure and future landsliding, the statistical landslide index model 2 envisages the landslide 

activities in the study area with 69% accuracy. It designates 54% and 15% of the landslide 

zones to the very high and high susceptibility classes. In addition, 23% of the unstable units 

are specified in the moderate risk zone, and about 9% in the low and very low susceptibility 

classes. 

5.2.1.3 Considering factors which show clear causative relationship with 

landslides (SI model 3) 

During the statistical weight analysis, the causative factors slope, lithology, landuse, aspect 

and slope shape gave a reasonable causative association with the inventory compared to the 

other factors. Therefore, a LSI map is created using those factors. The minimum LSI value 

obtained is -4.19 while the maximum is 2.52. The mean value is -0.34 and the standard 

deviation is 1.29. The cutoff landslide statistical index values are: 



‣ Moderate landslide susceptibility: -0.28 ≤ LSI < 0.16 

‣ High landslide susceptibility: 0.16 ≤ LSI < 0.32 




Figure 5.7 % of observed training landslides VS LSI (SI model3) 

The areal distribution of each susceptibility class and the final hazard map are shown in 

Figure 5.8 and Figure 5.9 respectively. It is observed that very low, low, moderate, high and 

very high susceptibility classes constitute 14.15%, 24.18%, 28.7%, 13.58% and 19.28% of the 

study area. With statistical index method of analysis, considering only the 5 factors which 

have a clear causative relationship with landslides, the total area of high and very high 

susceptibility classes is 33%. 

35 

30 

25 

20 

15 

10 

Area (%) 

5 

0 

Low ls. s. Very low ls. s. Moderate ls. s. High ls. s. Very high ls. s. 





To validate the final output, the hazard map is cross checked with validation landslide bodies. 

A cross operation is done between the validation landslides and the categorized susceptibility 

map. The result is shown in the Table 5.4. 

Table 5.4 Validation SI model 3 

Landslide susceptibility Area of validation landslides (Pix) Percentage 


Low 103 5.07 


High 367 18.10 

Very high 1126 55.53 

Total 2028 100.00 



The causative factors slope, lithology, Landuse, aspect and slope shape gave a reasonable 

causative association with the inventory. A landslide statistical index model performed 

considering only those factors as equally important to future instability detects the landslide 

incidences with 74% accuracy. It designates 56% and 18% of mass movement zones to the 

very high and high susceptibility classes. 20% of unstable zones are allotted to zones of 

moderate risk. If a landslide hazard analysis is performed using this model in Hagere Selam, it 

is highly probable that 6% of actual failure zones will not be recognized. 

5.2.2 Statistical weighting factor analysis 

The statistical index method considers each causative factor to be equally responsible for past 

landslides. It assumes future failure estimates is based on equal consideration of the inducing 

factors. Practically, however, this hardly will be true. A closer look at the result obtained from 

the weight-factor relationship (Table 5.1) shows significant differences between the 

importances of causative factors in triggering landslides. Statistical weighting factor analysis 

is a method that assigns data driven weights to each inducing parameter, so that the 

contribution of one parameter is different from the others (Cevik & Topal, 2003; Oztekin et 

al., 2005; Voogd, 1983). A weighting factor is calculated as: 

W = 

T − Min (T ) 

Max (T ) − Min (T ) 

(5.3) 

Where 

W = weighting factor calculated for layer, j. 

T = Total weighting index value of all landslide cells for layer, j 

Min (T ) = Minimum T value 

Max (T ) =Maximum T value 

The W values are calculated using ILWIS-GIS. The procedures followed are: 

 

 

The landslides database is cross multiplied with the w ij value map of each causative 

factor (parameter). 

The summed weight values of all cells within landslide bodies are calculated for each 

parameter. 



The resulting weighting factor values considering all the 10 inducing factors are given in 

Table 5.5. 

Table 5.5 Weighting factor values 

Causative factor (j) T j W fj 

Slope 1625.38 1.00 

Lithology 1609.95 0.99 

Elevation 1225.18 0.75 

Slope shape 51.14 0.02 

Distance to streams 23.00 0.00 

Distance to Road 17.50 0.00 

Aspect 367.33 0.22 

Land Use 125.30 0.07 

Distance to faults 96.40 0.05 

Wetness index 259.51 0.15 

From the weighting factor outputs, slope is the most influential factor to instability; 

lithology has nearly the same effect as slope. 

Elevation, aspect, wetness index and landuse have moderate but important 

contribution. 

Distances to road, and distance to stream have no significant contribution to 

landslides in the study area. 

The landslide susceptibility index is calculated using: 

 

LSI = W W 

 

(5.4) 

Where 

W = Weighting factor calculated for layer j. 

W ij = Weight of class i in parameter j 

n = number of causative factors combined 

5.2.2.1 SWF modeling results 

The procedure is similar to what was explained for the statistical index modeling, paragraph 

5.2.1, but the LSI map is calculated using equation 5.4. 



Figure 5.10 Percentage of observed training landslides versus LSI. Model 1 (A), Model 2 (B), and Model 3 (C) 



Table 5.6 Parameters of LSI map and cutoff values 

SWF_ Model 1 SWF_ Model 2 SWF_ Model 3 

Mean LSI -0.21 0.10 -0.15 

Min LSI -3.79 -3.44 -3.55 

Max LSI 2.30 2.06 2.12 

Standard deviation 0.95 1.03 1.06 

Cut off values 

Very low [-3.79, -0.90) [-3.44, -1.03) [-3.55, -1.20) 

Low [ -0.90, -0.55) [-1.03, -0.56) [-1.20, -0.58) 

Moderate [-0.55, -0.24) [-0.56, -0.32) [-0.58, -0.28) 

High [-0.24, 0.35) [-0.32, 0.07) [-0.28, 0.10) 

Very high [0.35, 2.30] [0.07, 2.06] [0.10, 2.12] 

Figure 5.10 presents the manual classification procedure to classify the numerical LSI values 

to a categorical map. Table 5.6 depicts the parameters of the entire LSI maps and boundary 

values used for the classification. The areal distribution of the landslide categories is given in 

Figure 5.6. 

35 

30 

25 

Area (%) 

20 

15 

10 

5 

SWF_model 1 

SWF_model 2 

SWF_model 3 

0 

Low Very low Moderate High Very high. 

Landslide susceptibility category 

Figure 5.11 Areal distribution of susceptibility classes 

From Figure 5.11, it is apparent that there are slight differences between the three approaches 

followed. Considering all the 10 selected factors, and employing statistical weighting factor 

method of analysis, the area identified as high and very high hazard zone is 32%. An 

additional 28.65% is regarded as a zone of possible failure with a moderate hazard. It is 

recalled that the area of landslides recognized during the inventory was about 20%. 



If only slope, lithology, distance to faults and landuse were the only factors responsible for the 

instability, the prediction of high and very high landslide susceptibility zones is 35.4%. In 

addition some 27% of the area, 135km², is a zone of possible failure with moderate hazard. 

If slope, lithology, landuse, aspect and slope shape are the only responsible factors for the 

mass movements in the study area, the high and very high hazard zone amounts to about 35%. 

In addition, the moderate landslide susceptibility zone is about 27.38% of the area. 

SWF model 2 and SWF model 3 are comparatively identical. However, SWF model 1 gives 

somewhat a smaller value. 



Figure 5.12 Landslide hazard map (a) SWF model 1, (b) SWF model 2 



Figure 5.13 Landslide hazard map (a) SWF model 3 

The accuracy of the results obtained using SWF models are verified by a cross validation of 

the final hazard maps with the validation landslides. Table 5.7 shows the results. 

Table 5.7 Validation of SWF models 

Landslide susceptibility Area of validation landslides (npix) Percentage 


Low 83 4.38 


High 335 17.67 

Very high 1136 59.92 

Total 1896 100.00 





Low 76 4.01 


High 396 20.89 

Very high 1214 64.03 

Total 1896 100.00 



Low 81 4.27 


High 361 19.04 

Very high 1205 63.55 

Total 1896 100.00 

Hence, considering all the 10 causative factors being variably important to failure and future 

landsliding, SWF model 1 recognizes the validation landslides in the study area with 78% 

accuracy. It specifies 60% and 18% of the actual mass movement zones in the very high and 

high susceptibility zones respectively. In addition, 17% of the incidences are regarded in the 

moderate hazard zone. The final method fails to identify 5% of the validation landslides. 

SWF model 2 and model 3 predict the landslide activities with 85% and 84% accuracies 

respectively. The failure to predict the validation landslides is about 5% for both models. 

Therefore, we conclude that varying the triggering factors improves the accuracy as far as 

weighting factor method is concerned. A model based on all triggering factors is proved to be 

less efficient than models based on selected triggering factors. 



5.2.3 Certainty factor analysis 

Among the commonly used GIS analysis models for landslide hazard mapping, certainty 

factor model has been profoundly considered and experimentally investigated to be beneficial 

(Chung & Fabbri, 1993; Chung & Leclerc, 1994; Binaghi et al., 1998). The Certainty Factor 

(CF) approach is a favorability function that handles the problem of combining many data 

layers, heterogeneity and uncertainty of input data (Chung & Fabbri, 1993). The Certainty 

Factor at each pixel p, denoted with CF k (p) is defined as the change in certainty that a 

proposition is true (i.e. an area is landslide prone) from without the evidence to given the 

evidence at p for each data layer (Chung & Leclerc, 1994; Binaghi et al., 1998). ‘No 

evidence’ means prior probability of having a landslide in a study area and ‘with evidence’ 

means the conditional probability of having a landslide given a certain class of a causative 

factor. 

Where: 

⎧ f − f 

⎪f (1 − f) if f ≥ f 

CF = 

⎨ f − f 

⎪ 

⎩f(1 − f ) if f < f 

CF ij - certainty factor given to class i of parameter j; 

f ij - the landslide density within the class i of parameter j; 

f - The landslide density within the entire map. 

(5.5) 

Statistically, f ij is the conditional probability having a number of landslide occurrence in class 

i of a causative factor j, and f is the prior probability of total number of landslide occurrences 

in the study area. 

The range of values of the CF is [−1, 1] and positive values indicate an increase in certainty, 

after evidence is observed, while negative numbers imply a decrease in certainty. A value of - 

1 indicates a maximum disfavoring effect and +1 show the strongest causative link between 

the class considered and landslides. A value close to 0 means that the prior probability is very 

similar to the conditional one, so it is not possible to give any indication about the certainty of 

the proposition. 



Table 5.1 gives the landslide densities for each category of the causative factors. Using 

equation 5.5, the certainty factor values are calculated on the basis of the conditions set. The 

results are presented in Table 5.8. 

Table 5.8 Certainty factor values 

Class 

Area of 

catagory 

Area of 

observed 

landslides f ij f CF 

(# of pixels) (# of pixels) 


Flat 13448 1367 0.10 0.21 -0.58 

Rolling 31512 5725 0.18 0.21 -0.17 

Mountanious 10664 3289 0.31 0.21 0.40 

Escarpments 2979 1912 0.64 0.21 0.85 

Cliffs 61 51 0.84 0.21 0.95 

Lithology 

Trap basalt 12766 3210 0.25 0.21 0.23 

Limestone Marl 5291 611 0.12 0.21 -0.49 

Amba Aradom Sandstone 3489 1340 0.38 0.21 0.59 

Shale 6019 891 0.15 0.21 -0.33 

Dolerite Sill 1877 942 0.50 0.21 0.74 

Shale Marl Limestone 10650 1102 0.10 0.21 -0.55 

Marl Limestone 12093 2888 0.24 0.21 0.18 

Adigrat Sandstone 5243 1292 0.25 0.21 0.21 

Alluvium 73 49 0.67 0.21 0.87 

Endaga Arbi Glacial 1189 0 0.00 0.21 -1.00 

Entcho Sandstone 1422 19 0.01 0.21 -0.95 

Elevation 

Low 729 0 0.00 0.21 -1.00 

Moderate 2842 0 0.00 0.21 -1.00 

Fairly Moderate 17438 2358 0.14 0.21 -0.39 

High 33827 8351 0.25 0.21 0.21 

Very High 5276 1634 0.31 0.21 0.42 

Slope shape 

Peak and Ridges 11267 2156 0.19 0.21 -0.08 

Saddle 15745 3380 0.21 0.21 0.05 

Slope Hill 25 5 0.20 0.21 -0.03 

Convex 8738 1947 0.22 0.21 0.10 

Concave 8774 1973 0.22 0.21 0.11 

Gully 13542 2595 0.19 0.21 -0.08 

Other 2021 288 0.14 0.21 -0.36 

Topographic wetness index 

WI


Class 

Area of 

catagory 

Area of 

observed 

landslides f ij f CF 

(# of pixels) (# of pixels) 

Distance to streams 

Very Close 7066 1381 0.20 0.21 -0.06 

Close 12703 2592 0.20 0.21 -0.01 

Moderate 11028 2321 0.21 0.21 0.03 

Fairly Moderate 12883 2784 0.22 0.21 0.06 

Far 12061 2534 0.21 0.21 0.03 

Very far 4371 732 0.17 0.21 -0.22 

Distance to road 

Close 3234 563 0.17 0.21 -0.18 

Medium 2902 503 0.17 0.21 -0.19 

Fairly Far 2661 562 0.21 0.21 0.03 

Very Far 51315 10716 0.21 0.21 0.02 

Aspect 

Flat 14 0 0.00 0.21 -1.00 

N 4686 1118 0.24 0.21 0.18 

NE 8815 1728 0.20 0.21 -0.06 

E 8147 1052 0.13 0.21 -0.43 

SE 7097 1354 0.19 0.21 -0.09 

S 8507 1612 0.19 0.21 -0.10 

SW 6647 1092 0.16 0.21 -0.24 

W 6433 1540 0.24 0.21 0.18 

NW 6263 1863 0.30 0.21 0.39 

N2 3436 983 0.29 0.21 0.36 

Landuse factor 

Bare Soils 39356 8844 0.22 0.21 0.11 

Sandy/Whitish Clay 3881 521 0.13 0.21 -0.40 

Urban 0 0 0.00 0.21 0.00 

Vegtation 16563 2919 0.18 0.21 -0.17 

Distance to faults 

Very Close 15140 2599 0.17 0.21 -0.20 

Close 12576 2480 0.20 0.21 -0.05 

Moderate 7851 1613 0.21 0.21 0.00 

Moderately far 5820 1410 0.24 0.21 0.19 

Far 7681 1958 0.25 0.21 0.24 

Very far 11044 2284 0.21 0.21 0.01 

From the results of the certainty factor calculation the following conclusions are drawn: 

The degree of certainty of a landslide incidence is the highest for cliffs. Slope angles 

> 15° are likely to fail, while slope angles < 15° are less prone to instability. 

Alluvium shows the highest CF value (0.87). Therefore, it is highly prone to mass 

movements. Dolerite sill, Amba Aradom sandstone and trap basalts show also very 

high CF values, indicating their susceptibility to landslides. Thus, failure mechanisms 

A and C, discussed in chapter 3, are well expressed by the analysis. Marl limestone 



formation shows a CF value of 0.18; however shale indicates a negative certainty 

factor value of -0.33. This is a discrepancy as far as landslide failure mechanism B is 

concerned. We believe, this has got to do with the large areal coverage of the shale 

formation. Though there are failures in this layer, there exist also equally many pixels 

with no landslide incidence that make the favorability function become negative. 

Elevation proves to be a strong casual factor to instability. Areas with elevation > 

2200 m are highly prone to failure while areas with elevation < 2220 m are not 

favorable for landslides. 

Concave slope shapes show a CF value of 0.11, thus are prone to failure. Convex 

shapes also have a positive contribution to mass movements. The other slope shapes 

indicate negative values close to zero. Therefore there is no indication that these 

shapes favor instability. 

Very far distance to a stream proves to be unlikely to failure. The other distance to 

stream classes do not have a strong casual link with landslides. 

The CF values for distance to road factor clearly show a weak causal link between the 

factor and instability. 

For aspect, a distinct result is obtained. North, Northwest and West facing slopes 

prove to be highly prone to instability. Opposite facing slopes, East, Southwest and 

South, are unfavorable for landslides. 

For distance to faults, the same result as obtained with the statistical index analysis is 

observed here. Recalling the same reasoning that was given in section 5.1, it is 

concluded that we haven’t observed a sufficient evidence for the causal relationship 

between faulting and mass movements. 

For landuse, it can be said that there is a negative effect of the presence of vegetation 

to slope instability while bare soils are susceptible to sliding. 

The CF is calculated for each inducing factor. The layers are then combined pairwise. The 

combination of two CF’s, X and Y, due to two different layers of information, is expressed as 

Z in equation 5.6 (Long, 2008). 



X + Y − XY if, X, Y ≥ 0 

⎧ 

X + Y 

Z = 

if X ∗ Y < 0 

⎨1 − min(/X/,/Y/) 

⎩ X + Y + XY if X, Y < 0 

(5.6) 

The pairwise combination of causative factors is performed repeatedly using a script written 

in ILWIS GIS using the integration rule of equation (5.6). The triggering factors to be 

considered are different for the three models speculated, as discussed earlier. 

5.2.3.1 CF modeling results 

Table 5.9 Parameters of the CF map and cutoff values 

CF_ Model 1 CF_ Model 2 CF_ Model 3 

Mean CF 0.05 0.15 0.03 

Min CF -1.00 -1.00 -1.00 

Max CF 1.00 0.99 0.99 

Standard deviation 0.81 0.74 0.74 

Cut off values 

Very low [-1, -0.856) [-1, -0.81) [-1, -0.74) 

Low [-0.86, -0.61) [-0.81, -0.61) [-0.74, -0.6) 

Moderate [-0.61, -0.03) [-0.61, -0.14) [-0.6, -0.26) 

Uncertain 0 0 0 

High [-0.03, 0.6) [-0.14, 0.4) [-0.26, 0.34) 

Very high [0.6, 1] [0.4, 0.99] [0.34, 0.99] 

The maximum CF value is 1, as obtained from CF model 1. Thus, the highest certainity for a 

landslide occurance is obtained when all triggering factors are taken into account. Figure 5.14 

demonstrates the procedure adopted to classify the discrete CF maps to catagorical landslide 

hazard maps. Figure 5.14D shows the areal distribution of the susceptibility classes. It is noted 

that CF_model 1 and model 2 predict over 40% of the area to be under landslide risk. This 

value exceeds the inventoried area by 99km². On the other hand CF_model 3 estimates the 

unstable area to be about 31%, which exceeds the inventoried area by 54km². the reliability of 

a model is assesed by looking into the extent by which it identifies the inventoried landslides. 

However, identifying the existing failures by itself is not a sufficient condition for the 

dependability of a model. This is because; an overestimated output could have a clean 

validation result. So, what we are looking for is a method that could efficiently identify the 

existing failures, and at the same time does not bring an overestimation to our results. 



Figure 5.14 Percentage of observed training landslides versus CF value: Model 1(A), Model 2(B), Model 3(c), and percentage area of susc. Classes (D) 



Figure 5.15 Landslide hazard map (a) CF model 1, (b) CF model2 



Figure 5.16 Landslide hazard map CF model 3 

The potential of the methods is judged by investigating the extent by which they can identify 

existing failures. Table 5.10 provides the results. 

Table 5.10 Validation of certainty factor method of analysis 



Low 120 6.33 


Uncertain 1 0.05 

High 558 29.43 

Very high 1098 57.91 

Total 1896 100 





Low 128 6.75 



High 523 27.58 

Very high 1081 57.01 

Total 1896 100 



Low 82 4.32 



High 530 27.95 

Very high 950 50.11 

Total 1896 100 

The highest accuracy is achieved when all triggering factors are considered. CF model 1 gives 

87.4% accuracy while CF model 2 and 3 yield 84.6% and 78.4% respectively. Hence, 

employing as many factors as possible gives a better potential to identify instabilities. 

5.2.4 Landslide susceptibility analysis 

Landslide susceptibility analysis is a method that is comparable to the landslide statistical 

index method of analysis. Unlike the statistical index method, it doesn’t express the weight of 

the triggering factors as a logarithmic function; instead the density relationship is outlined in a 

range of 100. The weighting factors are calculated on the basis of a comparison made 

between densities in each category with the total landslide density (Süzen & Doyuran, 2004): 

W = 100(f − f) (5.7) 

The result of the weight calculation is shown in Table 5.11. The landslide susceptibility is the 

cumulative effect of the weights of the triggering factors combined: 

 

LSI = W 

 

(5.8) 



Table 5.11 Weight values for the landslide susceptibility method 


Class Wij Class Wij Class Wij 

Flat -10.88 Low -20.54 Close -3.13 

Rolling -2.87 Moderate -20.54 Medium -3.20 

Mountanious 9.80 Fairly Moderate -7.01 Fairly Far 0.58 

Escarpments 43.14 High 4.15 Very Far 0.35 

Cliffs 62.56 Very High 10.44 

Lithology 

Elevation 

Slope shape 

Class Wij Class Wij Class Wij 

Trap basalt 4.61 Peak and Ridges -1.40 Flat -20.54 

Limestone Marl -8.99 Saddle 0.93 N 3.32 

Amba Aradom Sandstone 17.87 Slope Hill -0.54 NE -0.93 

Shale -5.73 Convex 1.75 E -7.62 

Dolerite Sill 29.65 Concave 1.95 SE -1.46 

Shale Marl Limestone -10.19 Gully -1.37 S -1.59 

Marl Limestone 3.35 Other -6.28 SW -4.11 

Adigrat Sandstone 4.11 W 3.40 

Alluvium 46.59 Distance to streams 

NW 9.21 

Endaga Arbi Glacial -20.54 Class Wij N2 8.07 

Entcho Sandstone -19.20 Very Close -0.99 

Close -0.13 

Topographic wetness index 

Moderate 0.51 

Class Wij Fairly Moderate 1.07 

WI


Figure 5.17 Landslide hazard map (a) LS (model 1), (b) LS (model2) 



Figure 5.18 Landslide hazard map LS (model 3) 

35 

30 

25 

Area (%) 

20 

15 

10 

LS_model 1 

LS_model 2 

LS_model 3 

5 

0 

Low Very low Moderate High Very high. 

Figure 5.19 Areal distribution of the hazard classes, LS method 

Slope Stability Analysis using GIS and Numerical Modeling Techniques


Table 5.12 presents the validation results of the predictions obtained with the LS 

susceptibility modeling. LS model 2 predicts 76% of the validation landslides accurately, 

while LS model1 and LS model 3 estimates only 74% of the failure bodies correctly. 

Therefore, employing all triggering factors is not as reliable as the approach based on only 

pre-estimated governing factors. 

Table 5.12 Validation results for landslide susceptibility method of analysis 

Landslide susceptibility 

Percentage area 

Ls_Model1 Ls_Model2 Ls_Model3 

Very low 0.95 0.58 0.63 

Low 6.06 6.43 4.64 

Moderate 18.87 17.82 20.88 

High 18.40 20.56 18.87 

Very high 55.72 54.61 54.98 

Total 100 100 100 


Chapter 6: Comparison and Conclusion 

Determining the landslide susceptibility of Hagere Selam area is the main objective of this 

research. In addition, different landslide models are tested to find the most suitable prediction 

method. In this chapter we will discuss: 

The comparison between the results of the employed methodologies and the inventory. 

A comparison between the accuracies of the models. 

Selection of a suitable physically based scenario. 

Selection of a suitable statistical model. 

Matching between statistical model outputs. 

Matching between the selected deterministic and statistical model results. 

Selection of the final representative hazard map. 

Identifying the responsible triggering factors. 

Discussing the final hazard map with respect to the inventory. 

Future work 

6.1 Comparison and discussion 

To compare the employed methodologies, the areal distribution of the assigned landslide 

hazard zones is examined prior to validating the outcomes. The total area of mass movement 

zone identified by aerial photo interpretation is 20%. Table 6.1 summarizes the areal 

distributions of stability classes defined by using the various methods. 

Table 6.1 Areal distribution summary (M x = model x) 


Physically based 

Statstical index 

Weighting factor 

Stability class LS susceptibility class Dry Half sat Sat M 1 M 2 M 3 M 1 M 2 M 3 

Unstable Very high 4.93 21.67 39.34 19.45 19.33 19.28 19.92 21.02 20.58 

Quasi stable High 6.42 12.85 13.65 13.51 12.07 13.58 10.53 14.34 14.66 

Moderately stable Moderate 8.39 13.02 12.39 25.93 31.51 28.7 29.74 27 27.38 

Stable Low/Very low 80.26 52.46 34.62 40.99 37.1 38.33 39.81 37.64 37.37 


Certainity factor Landslide susceptibility 

Stability class LS susceptibility class M 1 M 2 M 3 M 1 M 2 M 3 

Unstable Very high 16.87 19.51 14.57 17.11 16.87 17.53 

Quasi stable High 22.55 21.61 17.36 12.07 14.09 13.09 

Moderately stable Moderate 15.63 19.59 30.5 29.83 30.62 31.91 

Stable Low/Very low 42.65 39.22 37.53 40.99 38.42 37.47 


Chapter 6: Comparison and Conclusion 97 

Comparison of the landslide inventory and the methods is equally valuable as validating the 

outputs. An approach showing the highest matching with the observed landslides is not 

necessarily the best prediction method. An accuracy of close to 100% would be attained, if the 

hazard mapping overestimates the very high and high susceptibility zones. For instance, if the 

whole landslide pixels were used to classify the LSI values, only 2-3% of the landslides 

would have been unrecognized. However, it doesn’t mean that the output has 97-98% 

accuracy, rather it implies an overestimation. To prevent this, the classification is performed 

on training landslides (around 60% of the database), and the validation is done with the 

remaining 40% of the database. Hence, two perspectives shall be considered: 

A representative hazard map is at least expected to recognize failure zones indicated 

by the inventory, and at most anticipated to detect units with similar properties but not 

necessarily inside the inventory. 

As 20% of the area is identified by the morphology of the mobilized mass movements, 

its extent of susceptibility is expected to be very high or high. This is because once the 

soil mass is displaced, the first-time movement of the material causes the shear 

strength to drop from the original to the residual strength along the failure plane 

(Lambe & Whitman, 1979). 

Figure 6.1 Comparison of instability classes with the inventory (Mx = model x) 



From Figure 6.1, it is inferred that: 

The dry scenario indicates only 11% of the area to be unstable, a 9% offset from the 

inventory. Therefore, a physical model based on dry scenario is regarded as less 

appropriate. 

The saturated scenario shows a significantly larger amount of area to be susceptible as 

compared with all the other models. Hence, it over estimates the hazard. 

For the high and very high susceptibly classes, the deterministic methods yield larger 

values than the statistical methods. But, statistical approaches show more area to be 

unstable, if the moderate susceptibility category is included. 

The results of the half saturated condition demonstrate to be analogous to the certainty 

factor and weighting factor approaches. 

The Statistical index and susceptibility analysis methods give essentially similar 

results. 

Next, we will compare the prediction accuracies obtained by the models. By prediction 

accuracy, we mean the matching between the validation landslide set with the very high and 

high susceptibility categories. The results are depicted in Figure 6.2. 

Figure 6.2 Comparison between the accuracies of the employed methodologies (Mx=model x) 

It is noted from Figure 6.2 that: 

The physical model with fully saturated scenario and certainty factor model 1 give the 

highest accuracy (87%). Because the fully saturated condition envisages most of the 

area to be unstable, its high accuracy is regarded to arise from over estimation. 



On the other hand, the dry scenario gives the smallest matching with the database, an 

accuracy of 38%. Accordingly, its potential for slope stability analysis is judged to be 

limited. Still, it is critical in forecasting unconditionally unstable zones. 

The certainty factor and weighting factor approaches prove to be superior to the other 

statistical methods. The weighting factor model 2 and certainty factor model 2 give 

exactly the same accuracy (85%). 

The statistical index and landslide susceptibility approaches have more or less 

comparable validation results. 

The half saturated scenario predicts as many landslides as the statistical index and 

landslide susceptibility methods. Its accuracy is 74%. Owing to the fact that this 

approach is based on soil mechanics principles and that the half saturated condition 

represents practical field conditions, the result obtained is regarded to be highly 

appreciable. 

It is shown that for the weighting factor approach, the model based on the preestimated 

most governing factors give a better result than the model based on all the 

factors. On the contrary, for the certainty factor technique, the combination of all the 

factors proves to be better than the other combinations. 

The accuracies of the models tell how much of the landslides are predicted in the high and 

very high susceptibility categories. However, this is not adequate. The relative proportion of 

landslide bodies with respect to the area of the susceptibility class in which they exist is also 

important. This is done by calculating posterior probabilities (Long, 2008). The posterior 

probability is the area of observed landslides divided by the landslide susceptibility class in 

which they occur. The landslide prone zone identified by a certain method is regarded to be 

more accurately predicted, if its area of high and very high susceptibility class is small. If the 

posterior probability is small for low hazard zones, it means that the model has predicted large 

area to be a low risk zone and very few of the landslide bodies exist in the zone. Therefore, a 

low posterior probability in the low hazard zones, and large posterior probability in the high 

hazard zones depict the strength of a model in predicting instabilities. Table 6.2 presents the 

results of probability values calculated for each model. 



Table 6.2 Posterior probabilities of all methods 

Area 

(npix) (%) 

Landslide probability 

Deterministic 

Very low/low landslide susceptibility 31532 52.46 0.017 

Moderate landslide susceptibility 7827 13.02 0.048 

High landslide susceptibility 7727 12.85 0.060 

Very high landslide susceptibility 13026 21.67 0.083 

SI model 1 





SI model 2 





SI model 3 





WF model 1 





WF model 2 


Moderate landslide susceptibility 16232 27 0.023 



WF model 3 





CF model 1 







CF model 2 





CF model 3 





LS model 1 





LS model 2 





LS model 3 





From Table 6.2, CF model 1 gives the highest posterior probability of landslides in the very 

high susceptibility category (0.107). Therefore, it relatively predicts many landslide bodies in 

the very high hazard zone which has smaller area. In the same way, it has the smallest 

probability of landslide bodies in the low hazard zone (0.005). Such a comparison can also be 

made by drawing cumulative percentage of observed landslides against cumulative area. 

Figure 6.3 depicts the comparison made between all the employed methodologies. It is shown 

that CF model 1 has the highest curvature i.e. its classification gives the highest areas in the 

very low, low and moderate susceptibility classes. Hence, it proves to be more precise in 

predicting instabilities. 



Figure 6.3 Cumulative % of observed landslides versus cumulative % area 

Now, we will discuss matching between the employed methodologies. Correlation between 

different results can be weak or strong, i.e. the relationship between the two sets may be 

significant or weak. Identifying the agreed areas between different hazard maps gives a hint 

about the most important triggering factors. First, we will analyze the agreement between 

each three models for each statistical approach. Then, we will compare the representative 

models of each statistical approach with one another. Table 6.3 presents the percentage of 

agreed areas among the three models used for statistical approach. 

Table 6.3 Agreed areas on stability classes using statistical index method 

(SI_M1) vs ( SI_M2) (SI_M1) vs ( SI_M3) (SI_M2) vs ( SI_M3) 

(%) (%) (%) 

Very low 15 13 13 

Low 7 17 7 

Moderate 16 20 18 

High LS susc 6 8 6 

Very high 16 17 17 

Total agreed area 60 75 61 

The output hazard maps correlate strongly. Around 60% of the area is agreed by 

SI_M1 and SI_M2, 75% by SI_M1 and SI_M3, and 61% by SI_M2 and SI_M3. 



The correlations show the stability of the area controlled by factors common to the 

three models. It is agreed that 16-17% of the area is very high susceptibility class 

(VHLS), and 6-8% of the area is high susceptibility class (HLS). The areas defined by 

the combinations employed range between 19.28-19.45% for VHLS, and 12.07- 

13.58% for HLS. Thus, most of the failure zones are agreed. For instance, for SI_M1 

and SI_M3, the two out puts agree on about 90% of the landslide susceptible zones. 

As the factors slope, lithology and landuse are common to the models, they are 

regarded to significantly control the landslide mechanism in Hagere Selam. 

Table 6.4 gives the percentage of agreed areas among the three models of the statistical 

weighting factor approach. 

Table 6.4 Agreed areas on stability classes using statistical WF method 

(WF_M1) vs (WF_M2) (WF_M1) vs (WF_M3) (WF_M2) vs ( WF_M3) 

(%) (%) (%) 

Very low 15 12 13 

Low 13 15 20 





A landslide hazard mapping performed using SWF method gives the same result for 75% of 

the study area, if all the 10 causative factors are considered or only slope, lithology, landuse 

and fault density are considered. 76% of the results obtained by model 1 and 3 are alike. The 

highest matching is observed between model 2 and model 3, about 84% of the study area. 

More than 92% of the very high landslide susceptibility category is agreed by model 2 and 3. 

Therefore, we conclude that the triggering factors slope, lithology and landuse are indeed the 

most determining factors for instability. 

For the SWF approach, since the contribution of one parameter is different from the 

others, and data driven weights were given for all the factors, the final outputs match 

better than the other statistical methods. 



Table 6.5 presents the percentage of agreed areas among the three models of the certainty 

factor approach. 

Table 6.5 Agreed areas on stability classes using statistical CF method 

(CF_M1) vs (CF_M2) (CF_M1) vs (CF_M3) (CF_M2) vs (CF_M3) 

(%) (%) (%) 

Very low 20 15 14 

Low 11 10 11 





The outputs obtained using the three combinations of causative factors match for about 

71% of the area for combination 1 and 2, 59% for combination 1 and 3, and 62% for 

combination 2 and 3. As compared to SI and WF methods, the correlation for CF maps 

is lower. This means that certainty analysis is affected by the considered factors. The 

very high hazard unit shown by CF_M1 is 16.87% while for CF_M2 this is 19.51%. 

The percentage of agreed area on the very high hazard zone is 15% of the study area. 

This means that around 89% of the very high hazard unit identified by CF_M1 is also 

identified by CF_M2. So certainty analysis performed by considering only slope, 

lithology, landuse and distance to faults is as efficient as the analysis based on 10 

factors. 

Next we will discuss the comparison between each bivariate statistical technique. 

Table 6.6 Agreed areas on stability classes by four bivariate methods 

SI with WF SI with CF SI with LS WF with CF WF with LS 

% % % % % % 

Very low 17 16 16 18 18 23 

Low 17 11 13 10 12 16 

Moderate 21 11 21 12 21 9 

High LS susc 7 12 10 6 6 11 

Very high 17 18 17 16 16 17 

Total agreed area 79 68 77 62 73 76 

CF with LS 

SI = Statistical index, WF = Weighting factor, CF = certainty factor, LS = Landslide 

susceptibility method 



Some remarks can be noted: 

A maximum agreement of 79% is attained between the results of SI and WF methods, 

while the minimum agreement is 62% for WF and CF approaches. As compared with 

the other bivariate techniques, the certainty factor analysis yields somewhat different 

result. 

All four methods agree on 16-18% of the area to be very high susceptibility, and 6- 

12% as high susceptibility. The highest disagreements are observed for moderate and 

very low hazard zones. If we consider the areas agreed by all the statistical 

approaches, 22% of the area is likely to fail under normal circumstances or under 

minor destabilizing forces. An additional 9% is agreed by all the methods to be 

unstable under moderate destabilizing forces. 

To compare the agreement between physically based and statistical techniques, CF model 2 

and half saturated scenarios are selected. Table 6.7 shows the comparison. 

Table 6.7 Agreed areas on stability classes by physically based and certainty factor methods 


Agreed area 

Physically based(half sat) Certainity factor (%) 

Unstable /Quasi stable Very high/high 20.98 

Moderately stable Moderate 4.86 

Stable Low/Very low 28.12 

Total agreement 53.96 

The deterministic and certainty factors approaches agree on 21% of the area to be unstable or 

quasi stable, and 28% to be stable. The disagreement is pronounced for the moderately stable 

category. Though the statistical methods match sufficiently with each other, the similarity 

between half saturated physically based model and certainty factor approaches is noticeably 

small. Apparently, the reason is the disparity between the core concepts the models are based 

up on. However, Figure 6.4 demonstrates that most of the landslide source areas are in zones 

of agreement. Therefore, it can be said that there is no significant difference between the two 

methods as far as present landsliding is concerned. 



Figure 6.4 A map displaying matching between statistical and deterministic methods 

6.2 Conclusions 

For a long time, the existence of landslides was not recognized, and landsliding was not 

believed to be a significant hazard in the 497 km² study area in Hagere Selam, northern 

Ethiopia. However, during the last few years, slope instabilities were reported, and studies 

showed that many of the failures were reactivations of old deep-seated landslides. Because 

slope instability problems are affecting human lives, infrastructures, agricultural land, and the 

natural environment, a landslide risk assessment study is deemed to be indispensable. 



We developed a landslide inventory of the study area consisting of cliffs associated with rock 

falls and landslides zones associated with debris flows, debris slides, rock slides and slumps. 

30 debris flows, 7 landslide belts, 4 landslides in the Antalo supersequence, 12 landslides in 

Imba Degua and Chini ridges, and 2 slumps were identified. The landslide database amounts 

to about 20% of the study area. 

Four failure mechanisms are believed to be responsible for the landsliding in the study area. 

The first mechanism consists of flow of geologic layers resting over the Amba Aradom 

sandstone; this is mainly the basaltic flows. Secondly, the landslides affecting the Antalo 

limestone are related to pore water pressure built up resulting from the massive/ hard layers 

acting as an aquicludes or aquitard. Third, landslides composed of fresh dolerite boulders 

resting over corestone weathering profiles. Lastly, rock falls arise from the detachment and 

toppling of boulders from rocky cliffs after every rainy season. 

A deterministic approach for landslide susceptibility mapping was applied. Infinite slope 

models including three steady state conditions assuming either dry, half saturated, or saturated 

conditions were analyzed. A comprehensive evaluation of the obtained landslide susceptibility 

maps was done by model validation whereby the outputs are justified by comparing them with 

the landslide database. The following conclusions are drawn: 

The fully saturated scenario model validation result is 88%, but is regarded to arise 

from an over-estimation of instability. Such a simulation is judged to be unpractical. 

Nonetheless, such slope stability analysis is essential, as it correctly delineates the 

unconditionally stable zones. 

As expected, the dry scenario demonstrated a low validation result, i.e. an under 

estimation to slope instability. However, it is crucial in identifying the unconditionally 

unstable zones. 

The half saturated scenario provides the best output among the physically based 

models. It correctly designates 75% of the inventoried landslides. The total area of 

landslide hazard zones defined by this model is 35%. It proved even to be better than 

some of the statistical models. Therefore, among the deterministic methods, it is 

believed to be the most suitable for the assessment of landslide incidence in the study 

area. 



A combined factor of safety map obtained from the three steady state scenarios is 

believed to be important, because each scenario has its particular importance as 

discussed above. 

The infinite slope model detected landslides in the Amba Aradom sandstone, and rock 

falls in Adigrat sandstone taking place in the NW part of the study area. Slope 

instability due to increase in soil moisture was markedly recognized in the basalts. 

The behavior of the expansive clays resulting from the weathering of basalts favor an 

enhanced pore-water pressure built up, which eventually leads to an increase in 

landslides in the formation. 

In addition, 4 statistical approaches, statistical index, weighting factor, certainty factor and 

landslide susceptibility analysis, were applied. These methods depend mainly upon a 

relationship between past landslides and causative factors. Ten triggering factors were 

selected. From the correlation between the observed landslides and the factors, the following 

conclusions are drawn: 

Slope angle has a clear causal relationship with landslides. It is concluded that 

landslides occur in the study area in zones with slope angle > 15°. 

Alluvium, Dolerite sill, Amba Aradom sandstone, and Trap basalts have soil 

properties favorable for instability. 

Elevation proved to be a strong causal factor to instability. Areas with elevation > 

2200 m are highly prone to failure, while areas with elevation < 2220 m are not 

favorable for landslides 

Concave slope shapes proved to be correlated with landslides better than other shapes, 

though the correlation is weak. Therefore, the results obtained do not allow drawing 

any meaningful conclusion here. 

Distance to river factor does not have a strong causal link with landslides. Hence, 

undercutting of the slope by a river was probably not the main triggering factor for the 


Destabilizing of slopes by road construction is judged to be less significant in the 

study area. 

With aspect, a distinctive result is obtained. North, Northwest and West facing slopes 

proved to be highly prone to instability, and quite the opposite for slopes facing East, 

Southwest and South. 



We believe that though faulting is an important inducing factor for destabilizing 

slopes, landslides are generally not located in fault zones in the study area. Hence, it is 

concluded that there is no sufficient evidence for a causal relationship between 

faulting and mass movements. 

For landuse, there is a negative effect of the presence of vegetation to slope instability, 

while bare soils are susceptible to sliding. 

For the application of statistical methods, three different causative factor combinations were 

employed. Accordingly, the following conclusions are drawn: 

Certainty factor model 1 is selected to be the best model for predicting landsliding in 

the study area. It is based on causal relationships between past landsliding and 10 

selected triggering factors. It predicts 41% of the area to be under landslide risk. The 

quality of the output is successfully tested by validating it with randomly chosen 

landslide grid cells. It correctly identifies 87% of the landslide units. 

The hazard maps from the statistical index, weighting factor and landslide 

susceptibility methods, are highly similar, as indicated by an average of 75% of the 

area being classified in the same susceptibility class. 

The statistical index and landslide susceptibility methods confirm to be less powerful 

than the other statistical models. 

Altering factors involved in the combinations produces significantly different outputs. 

For SI and CF models, the combination based on the entire causative factors yield an 

enhanced recognition of the existing landslides. On the contrary, for SWF and LSS 

methods the combination based on slope, lithology, land use and fault density result in 

a better identification of the existing landslides. Moreover, CF method yields 

relatively more disparate results for the employed three combinations. 

Finally, Figure 6.5 gives the selected hazard maps based on certainty factor model 1 and a 

combination of three steady state scenarios. Based on the CF model 1, it is concluded that 

41% of the area is under high and very high landslide risk, 15% of the area is moderately 

susceptible, and 44% of the area is free from landslide risk. Based on the combined steady 

state deterministic model, it is concluded that 34.6% of the study area is unconditionally 

stable, and will not fail under any circumstances unless major destabilizing activities occur. If 

extreme rainfall event leads to full soil saturation, 12.4% of the area will face instability under 

moderate destabilizing actions, 13.7% of the area will likely fail under minor destabilizing 



forces, and 17.7% of the area needs stabilization methods to control instability. Landslides 

will be mobilized in about 16.7% of the area, if the soil is half saturated; 4.93% of the area 

will likely fail under any condition, and hence stabilization techniques are recommended. 



Figure 6.5 Final Hazard maps based on (A) CF model 1, and (B) combined steady state deterministic methods 



6.3 Recommendations 

Our understanding of the geotechnical and geomorphologic mechanisms of slope instability 

would be enhanced, if the following points were thoroughly assessed: 

A detailed soil investigation including laboratory analyses. 

Circular stability calculations on typical sections by various methods. 

A compliment of other slope stability softwares together with GIS. 

A zoomed analysis on selected section to understand the soil mechanics behind it. 

A consideration of soil wetness based on different return periods of rainfall. 


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Appendix 123 

Appendix 

Figure 1 VNIR image 

Figure 2 SI weight values 


Appendix 124 

Figure 3 SI weight for geology category 

Table 1. Example of weight values with % of landslides 

SI(model2) Total pix % of observed LS CF model 2 % of observed LS 

-2.754 1 0.02 -0.957 0.02 

-2.451 3 0.07 -0.946 0.07 

-2.251 20 0.44 -0.926 0.44 

-2.173 21 0.47 -0.916 0.47 

-2.142 22 0.49 -0.916 0.49 

-2.033 23 0.51 -0.9 0.93 

-2.027 24 0.53 -0.897 0.95 

-1.951 44 0.98 -0.895 1.04 

-1.933 46 1.02 -0.886 1.07 

-1.873 47 1.04 -0.877 1.09 

-1.87 51 1.13 -0.855 1.75 

-1.844 52 1.15 -0.852 1.8 

-1.67 82 1.82 -0.843 1.82 

-1.644 88 1.95 -0.836 2.37 

-1.594 89 1.98 -0.831 2.51 


Appendix 125 

-1.561 114 2.53 -0.818 2.53 

-1.555 115 2.55 -0.803 4.77 

-1.446 121 2.69 -0.797 4.9 

-1.37 222 4.93 -0.795 4.95 

-1.344 239 5.30 -0.789 5.08 

-1.341 245 5.44 -0.778 5.7 

-1.314 247 5.48 -0.771 6.08 

-1.296 248 5.50 -0.761 6.1 

-1.261 276 6.13 -0.753 6.17 

-1.255 279 6.19 -0.726 6.24 

-1.2 282 6.26 -0.717 6.41 

-1.146 285 6.32 -0.712 6.48 

-1.141 306 6.79 -0.708 6.95 

-1.117 308 6.84 -0.706 6.97 

-1.114 316 7.01 -0.705 7.01 

-1.111 319 7.08 -0.677 7.23 

-1.094 320 7.10 -0.67 7.48 

-1.063 323 7.17 -0.66 7.52 

-1.032 334 7.41 -0.633 7.57 

-1.026 336 7.46 -0.617 8.23 

-0.996 346 7.68 -0.605 9.59 

-0.917 358 7.94 -0.603 9.61 

-0.915 360 7.99 -0.598 10.3 

-0.911 378 8.39 -0.593 10.56 

-0.841 439 9.74 -0.583 10.61 

-0.814 469 10.41 -0.554 11.07 

-0.784 470 10.43 -0.531 11.3 

-0.782 472 10.47 -0.53 11.52 

-0.733 503 11.16 -0.507 11.58 

-0.732 524 11.63 -0.504 11.7 

-0.726 529 11.74 -0.497 11.94 

-0.715 539 11.96 -0.467 11.96 

-0.66 549 12.18 -0.453 12.14 

-0.617 562 12.47 -0.449 12.43 

-0.611 570 12.65 -0.445 13.69 

-0.608 581 12.89 -0.435 14 

-0.605 582 12.92 -0.404 14.07 

-0.584 590 13.09 -0.365 14.6 

-0.533 647 14.36 -0.352 15.05 

-0.482 661 14.67 -0.32 15.45 

-0.46 681 15.11 -0.305 15.82 

-0.415 705 15.65 -0.278 15.93 

-0.408 729 16.18 -0.259 16.36 

-0.359 739 16.40 -0.248 20.88 

-0.305 744 16.51 -0.222 20.99 

-0.293 748 16.60 -0.215 21.22 


Appendix 126 

-0.284 767 17.02 -0.192 21.53 

-0.253 769 17.07 -0.162 21.57 

-0.233 973 21.59 -0.122 24.28 

-0.204 987 21.90 -0.079 24.46 

-0.203 992 22.02 -0.078 24.81 

-0.186 994 22.06 -0.059 27.1 

-0.184 996 22.10 -0.059 27.14 

-0.161 1003 22.26 -0.012 27.43 

-0.16 1125 24.97 0.017 27.59 

-0.108 1251 27.76 0.044 27.83 

-0.082 1254 27.83 0.059 28.05 

-0.079 1270 28.18 0.069 29.36 

-0.059 1280 28.41 0.103 29.54 

-0.027 1293 28.70 0.119 29.69 

-0.024 1304 28.94 0.147 29.85 

-0.004 1312 29.12 0.208 29.87 

-0.003 1371 30.43 0.214 31.05 

0.015 1374 30.49 0.215 31.36 

0.047 1381 30.65 0.225 31.42 

0.107 1386 30.76 0.266 32.29 

0.114 1400 31.07 0.274 32.6 

0.116 1402 31.11 0.294 33.09 

0.121 1455 32.29 0.308 33.4 

0.139 1469 32.60 0.312 37.97 

0.17 1470 32.62 0.321 38.06 

0.173 1509 33.49 0.338 38.59 

0.176 1510 33.51 0.342 38.62 

0.199 1513 33.58 0.366 38.64 

0.22 1527 33.89 0.37 39.15 

0.276 1549 34.38 0.388 39.19 

0.296 1573 34.91 0.392 39.3 

0.297 1779 39.48 0.393 39.33 

0.315 1787 39.66 0.419 50.47 

0.373 1788 39.68 0.428 50.64 

0.407 1834 40.70 0.434 50.71 

0.42 1896 42.08 0.458 56.24 

0.421 2398 53.22 0.46 56.3 

0.45 2401 53.28 0.496 56.39 

0.47 2409 53.46 0.499 57.77 

0.473 2658 58.99 0.524 58.99 

0.48 2659 59.01 0.531 59.17 

0.488 2660 59.03 0.537 60.94 

0.499 2688 59.65 0.548 60.99 

0.502 2692 59.74 0.551 62.01 

0.505 2747 60.96 0.572 62.03 

0.526 2827 62.74 0.605 62.14 


Appendix 127 

0.529 2834 62.89 0.606 62.16 

0.596 2839 63.00 0.609 62.92 

0.65 2873 63.76 0.609 62.98 

0.688 2907 64.51 0.616 63.23 

0.702 2928 64.98 0.63 66.91 

0.705 2929 65.00 0.635 67.38 

0.72 3095 68.69 0.648 68.89 

0.726 3106 68.93 0.656 68.95 

0.729 3108 68.97 0.658 71.68 

0.749 3111 69.04 0.69 72.44 

0.78 3119 69.22 0.692 72.59 

0.805 3187 70.73 0.703 72.61 

0.826 3310 73.46 0.708 73.86 

0.896 3366 74.70 0.711 75.9 

0.949 3396 75.37 0.711 76.52 

0.95 3488 77.41 0.73 79.21 

0.988 3718 82.51 0.751 79.87 

1.002 3839 85.20 0.771 84.98 

1.028 3842 85.26 0.777 85.02 

1.029 3859 85.64 0.781 85.2 

1.217 3860 85.66 0.816 86.62 

1.235 3862 85.71 0.835 87 

1.238 3968 88.06 0.846 87.02 

1.249 4032 89.48 0.855 87.33 

1.259 4073 90.39 0.856 87.39 

1.383 4109 91.19 0.875 87.44 

1.425 4123 91.50 0.882 89.79 

1.435 4131 91.68 0.885 90.7 

1.438 4133 91.72 0.886 92.83 

1.459 4138 91.83 0.902 92.88 

1.517 4234 93.96 0.903 93.68 

1.538 4342 96.36 0.905 93.79 

1.546 4343 96.38 0.909 93.96 

1.559 4375 97.09 0.912 96.36 

1.682 4387 97.36 0.915 97.07 

1.683 4439 98.51 0.928 98.22 

1.735 4485 99.53 0.933 99.25 

1.846 4491 99.67 0.938 99.51 

1.982 4498 99.82 0.954 99.67 

2.25 4506 100.00 0.969 99.69 

0.972 99.87 

0.977 100

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