Download - Academy Publisher

More documents

Recommendations

Info

Start Read DEM ∑ k∈Si E i k h( i) = (4) S Y Detect nodes without flow direction and put them into the array All nodes in the array are processed N An unprocessed node in the array is put into the open list Select the node with the minimum (i) f value in the open list Add the selected node into the closed list and remove it from the open list The selected node is an outlet N Check the adjacent nodes of the selected node in the open list Add the adjacent nodes which are neither in open list nor in closed list into open list Establish pointers from these nodes to the selected node Traced from the outlet back to the starting node in the closed list and adjust the elevations Generating flow direction matrix Y Where f (i) is heuristic information value of node i , g (i) is the difference from the node i to the starting node, h (i) is estimated cost, the arithmetic average elevation of the cells in n-by-n (n=3, 5, 7, …) window centered the node i , we choose 5 in this article, E s is the elevation value of the starting node s , E i is the elevation value the node i , E k is the elevation value the node k , S i is a set which contains all the nodes in n-by-n window centered the node i , k is a element of set S i , and S i is the number of nodes in the set S i . IV EXPERIMENT AND RESULTS To examine the suitability and performance of the proposed algorithm, we implement it in Pascal and make experiments on actual DEM data. For presentation purpose all raster maps are converted into vector format. The study area is “1-degree” DEM which was provided by the US Geological Survey [22]. The 1-degree DEM is 1:250,000-scale Digital Elevation Models and covers an area of 1° in longitude by 1°latitude. The basic elevation model is produced by the Defense Mapping Agency (DMA) using cartographic and photographic sources. The 1-degree DEM consists of a regular array of elevations referenced horizontally on the geographic coordinate (latitude/longitude) system of the World Geodetic System 1984 Datum. Elevations are in meters relative to mean sea level. The DEM contains 601 rows by 1201 columns and spacing of the elevations along and between each profile is 3 arc-seconds. Fig. 2 shows the drainage networks generated using ArcGIS 9.2. The major drainage could be delineated satisfactorily but the spurious parallel flow channels can be seen and the detailed information is lost. It is obvious in the selected rectangle and circle region in Fig. 2. It is due to the error in selecting the flow direction of pits and flats when more than one possible direction exists. Fig. 3 shows the proposed method for extracting drainage network from the same DEM data. It can be seen from Fig. 3, continuous drainage network and major drainage are delineated. Generating flow accumulation matrix Extracting drainage networks End Figure 1. Schematic diagram showing the proposed method. g( i) = E i − E s (3) Figure 2. Drainage networks generated using ArcGIS 9.2. 114
Figure 3. Drainage networks generated using the proposed method. The detailed information can be retrieved and the spurious parallel flow lines can not be found. In order to avoid these cases, the proposed algorithm takes heuristic information for hydrologic analysis and tries to capture the topography and assign the optimal flow direction to the spurious pits and flats. V CONCLUSION AND DISCUSSION This paper presents a new method to assign the direction of the flow through pits and flats by searching for minimum value of heuristic function. The proposed algorithm has been tested with actual DEM data, and the experimental evidence suggests that the proposed algorithm can extract drainage networks better and fully than the basic method. To compensate inadequate searching information of other methods, the proposed method finds the outlet with heuristic information. Furthermore, the proposed method can handle pits and flats effectively in one procedure. ACKNOWLEDGMENT The work is supported by Science Foundation for Young Teachers of Northeast Normal University. REFERENCES [1] D. M. Wolock, and G. J. McCabe, “Comparison of single and multi-flow direction algorithms for computing topographic parameters in TOPMODEL,” Water Resources Research, 31 (5), pp. 1315–1324, 1995. [2] T. G. Freeman, “Calculating catchment area with divergent flow based on a regular grid,” Computers & Geosciences, 17 (3), pp. 413–422, 1991. [3] Z. Li, Q. Zhu, and C. Gold, Digital Terrain Modeling: Principles and Methodology, Boca Raton: CRC Press, 2004, pp. 323. [4] J. F. O’Callaghan, and D. M. Mark, “The extraction of drainage networks from digital elevation data,” Computer Vision, Graphics, and Image Processing, 28, pp. 323-344, 1984. [5] S. K. Jenson, and J. O Domingue, “Extracting topographic structures from digital elevation data from geographic information system analysis,” Photogrammetric Engineering and Remote Sensing, 54 (11), pp. 1593-1600, 1988. [6] F. Kenny, B. Matthews, and K. Todd, “Routing overland flow through sinks and flats in interpolated raster terrain surfaces,” Computer & Geosciences, 34, pp. 1417-1430, 2008. [7] L. W. Martz, J. Garbrecht, “The treatment of flat areas and depressions in automated drainage analysis of raster digital elevation models,” Hydrological Processes, 12, pp. 843- 855, 1998. [8] L. W. Martz, and J. Garbrecht, “Automated extraction of drainage network and watershed data from digital elevation models,” Water Resources Bulletin, 29 (6), pp. 901–908, 1993. [9] W. Zeng, and R. L. Church, “Finding shortest paths on real networks: the case for A*,” International Journal of Geographical Information Science, 23 (4), pp. 531-543, 2009. [10] J. Lindsay, and I. F. Creed, “Sensitivity of digital landscapes to artifact depressions in remotely-sensed DEM,” Photogrammetric Engineering and Remote Sensing, 71 (9), pp. 1029–1036, 2005. [11] L. E. Band, “Topographic partition of watersheds with digital elevation models,” Water Resources Research, 22 (1), pp. 15–24, 1986. [12] L. W. Martz, and J. Garbrecht, “An outlet breaching algorithm for the treatment of closed depressions in a raster DEM,” Computers & Geosciences, 25, 835–844, 1999. [13] L. W. Martz, J. Garbrecht, “Numerical definition of drainage network and the sub-catchment areas from digital elevation models,” Computers and Geosciences, 18 (6), pp. 747–761, 1992. [14] A. Tribe, “Automated recognition of valley lines and drainage networks from grid digital elevation models: a review and a new method,” Journal of Hydrology, 139, pp. 263–293, 1992. [15] J. Richard, “Algorithms for using a DEM for mapping catchment areas of stream sediment samples,” Computers & Geosciences, 28 (9), pp. 1051-1060, 2002. [16] M. F. Hutchinson, “A new procedure for gridding elevation and stream line data with automatic removal of spurious pits,” Journal of Hydrology, 106, pp. 211–232, 1989. [17] J. Fairfield, and P. Leymarie, “Drainage networks from grid digital elevation models,” Water Resources Research, 27(5), pp.709-717, 1991. [18] D. G. Tarboton, “A new method for the determination of flow directions and contributing areas in grid digital elevation models,” Water Resources Research, 33, pp. 309-319, 1997. [19] R. Jana, T. V. Reshmidevi, P. S. Arun, and T. I. Eldho, “An enhanced technique in construction of the discrete drainage network from low-resolution spatial database,” Computer & Geosciences, 33, 717-727, 2007. [20] CatchmentSIM,http://www.toolkit.net.au/catchsim/Overvie w/PFSMethod/PFSMethod.htm, last access: 13 March 2007, 2007. [21] S. J. Russel, and P.Norvig, Artificial Intelligence – A Modern Approach, Pearson Education, Inc., USA: New Jersey, 2003. [22] USGS, http://edcftp.cr.usgs.gov/pub/data/DEM/250, last access: 13 March 2007. 115
Page 1 and 2:
Proceedings The Second Internationa
Page 3 and 4:
Table of Contents Message from the
Page 5 and 6:
Zuming Xiao, Zhan Guo, Bin Tan, and
Page 7 and 8:
Message from the Symposium Chairs T
Page 9 and 10:
Second International Symposium on N
Page 11 and 12:
ISBN 978-952-5726-09-1 (Print) Proc
Page 13 and 14:
model that can deal with time serie
Page 15 and 16:
ISBN 978-952-5726-09-1 (Print) Proc
Page 17 and 18:
training for the Wushu competition
Page 19 and 20:
ISBN 978-952-5726-09-1 (Print) Proc
Page 21 and 22:
information, called weak uncertain
Page 23 and 24:
Student side Student side Student s
Page 25 and 26:
ISBN 978-952-5726-09-1 (Print) Proc
Page 27 and 28:
If we define element of student as
Page 29 and 30:
ISBN 978-952-5726-09-1 (Print) Proc
Page 31 and 32:
QoS, each frame data is divided int
Page 33 and 34:
ISBN 978-952-5726-09-1 (Print) Proc
Page 35 and 36:
for Imaging Two and Three Phase Flo
Page 37 and 38:
ISBN 978-952-5726-09-1 (Print) Proc
Page 39 and 40:
A. Profiling&following control algo
Page 41 and 42:
Research and Realization about Conv
Page 43 and 44:
PDF document structure is a tree st
Page 45 and 46:
ISBN 978-952-5726-09-1 (Print) Proc
Page 47 and 48:
support 10 Mb / s. But ENC28J60 onl
Page 49 and 50:
ISBN 978-952-5726-09-1 (Print) Proc
Page 51 and 52:
condition the first byte output of
Page 53 and 54:
ISBN 978-952-5726-09-1 (Print) Proc
Page 55 and 56:
According to maximum membership deg
Page 57 and 58:
ISBN 978-952-5726-09-1 (Print) Proc
Page 59 and 60:
ubber according to the mass ratio o
Page 61 and 62:
Ⅲ. AN IMPROVED DNA ALGORITHM FOR
Page 63 and 64:
Ⅴ.CONCLUSION REMARKS DNA computer
Page 65 and 66:
its first child q 1 on the left, th
Page 67 and 68:
chains can greatly improve efficien
Page 69 and 70:
II. RELATED WORK A. Mobile Service
Page 71 and 72:
special services. SOAP is used to b
Page 73 and 74: detection methods of DDoS attacks m
Page 75 and 76: Step4 calculate the new subordinate
Page 77 and 78: Figure 1. An analysis of the partit
Page 79 and 80: ISBN 978-952-5726-09-1 (Print) Proc
Page 81 and 82: The preceding three formulas can be
Page 85 and 86: And the decay speed of buffer seque
Page 89 and 90: distance of view point. Given that
Page 93 and 94: Ⅳ. EVALUATION OF BLENDED LEARNING
Page 97 and 98: symmetric with respect to the origi
Page 101 and 102: each sample belongs to each categor
Page 105 and 106: A. Data Preparing To generate our t
Page 109 and 110: oth α and β . determination of Qu
Page 113 and 114: Strong earthquake 0.1< M L
Page 117 and 118: indirect causes, and the logical re
Page 119 and 120: egression. Granger causality test i
Page 121 and 122: B. Evaluation We have evaluated thi
Page 123: used as a source and neighboring ce
Page 127 and 128: Combinational logic unit failures i
Page 129 and 130: Tabal.1 Combinational fault logic t
Page 131 and 132: under the endorsement of both the m
Page 133 and 134: TABLE I. DESCRIPTION OF PROPOSITION
Page 135 and 136: Figure 2. The process of the invers
Page 137 and 138: Figure 9. (a)the original image.(b)
Page 141 and 142: In order to reduce to the number of
Page 145 and 146: that studying being going to be to
Page 147 and 148: Reference[10] analyzed the evolutio
Page 149 and 150: module, communication module, apper
Page 151 and 152: After the comprehensive performance
Page 153 and 154: system testing can be seen that the
Page 155 and 156: III. AUTONOMIC RESOURCE ALLOCATION
Page 157 and 158: The QoE i (T i ) is the ith user’
Page 161 and 162: nodes will be formed one cluster, t
Page 165 and 166: Where n is the number of data point
Page 169 and 170: Keyboard event Application User mod
Page 173 and 174: SQL Azure will eventually include a
Page 175 and 176:
ISBN 978-952-5726-09-1 (Print) Proc
Page 177 and 178:
The nodes in the suffix tree are dr
Page 179 and 180:
[3] Y. Li, S. M. Chung, and J. D. H
Page 181 and 182:
some drilling fluid produces hydrog
Page 183 and 184:
"normalization", whose membership b
Page 185 and 186:
and minimum structural elements in
Page 187 and 188:
esults of the spatial transform par
Page 189 and 190:
B. Research on protocol actions Com
Page 191 and 192:
ACKNOWLEDGMENT This work is funded
Page 193 and 194:
A. Problem Description In a small b
Page 195 and 196:
[4] Huang, Hung, and J. Y. jen Hsu.
Page 197 and 198:
Further by calculating, the followi
Page 199 and 200:
TABLE II. THE CONCENTRATION BETWEEN
Page 201 and 202:
private key that obtained by using
Page 203 and 204:
ISBN 978-952-5726-09-1 (Print) Proc
Page 205 and 206:
ontology, and the domain dictionary
Page 207 and 208:
ISBN 978-952-5726-09-1 (Print) Proc
Page 209 and 210:
Eq.4 is NP-hard and can be solved b
Page 211 and 212:
ISBN 978-952-5726-09-1 (Print) Proc
Page 213 and 214:
Where: G i is the selection field o
Page 215 and 216:
If there is X j in a generation, wh
Page 217 and 218:
Theorem 2.4([10]). Let L 1 and L 2
Page 219 and 220:
The relation between the lattice im
Page 221 and 222:
Figure 2. Example of single-step de
Page 223 and 224:
evocation and the fourth group stor
Page 225 and 226:
focused mainly on rule-based forms
Page 227 and 228:
Ⅵ. CONCLUSION Figure 6. The Flask
Page 229 and 230:
EDCF in comparison with DCF, has so
Page 231 and 232:
B. Simulation results and analysis
Page 233 and 234:
ISBN 978-952-5726-09-1 (Print) Proc
Page 235 and 236:
in routing table. When search resou
Page 237 and 238:
ISBN 978-952-5726-09-1 (Print) Proc
Page 239 and 240:
others through the selective emotio
Page 241 and 242:
such as weather information, techno
Page 243 and 244:
the opening window, a related page
Page 245 and 246:
1) Process context storage areas. I
Page 247 and 248:
V. CONCLUSION In this thesis, sCPU-
Page 249 and 250:
main types of horizontal search env
Page 251 and 252:
Enterprise Portal security provides
Page 253 and 254:
() t = [ S () t S () t ] T N S ,...
Page 255 and 256:
[2] Kumaravel, N., and Kavitha, V.,
Page 257 and 258:
output, so BP network has been wide
Page 259 and 260:
input to the artificial neural netw
Page 261 and 262:
(2) In a process (tokens from outsi
Page 263 and 264:
The reduction process consists of t
Page 265 and 266:
all eight normal vectors are identi
Page 267 and 268:
is B object , and the number of emp
Page 269 and 270:
xi, j y' = εα ( (1 + tanh( )) −
Page 271 and 272:
Error 8000 7000 6000 5000 4000 3000
Page 273 and 274:
Architecture (AMBA) a new bus archi
Page 275 and 276:
In order to ensure the smooth proce
Page 277 and 278:
ISBN 978-952-5726-09-1 (Print) Proc
Page 279 and 280:
In this paper, we adopt two Sobel o
Page 281 and 282:
ISBN 978-952-5726-09-1 (Print) Proc
Page 283 and 284:
four layers.The far right of the gr
Page 285 and 286:
ISBN 978-952-5726-09-1 (Print) Proc
Page 287 and 288:
TABLE II. OPERATIONAL EMPLOYEE TABL
Page 289 and 290:
A. Object-relational Type To audit
Page 291 and 292:
to execution time. Also, DBA would
Page 293 and 294:
Liping Chen .......................
show all

Download - Academy Publisher

Create successful ePaper yourself

Delete template?

Save as template?