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each link having a predetermined weight. • A model: this is the information processing component of the neural network • A learning algorithm: this is the set rules within which the network will learn based on the training data provided Figure 5.1 Single Layer Feed Forward Network (Meyer-Baese, 2009) There are many different types of artificial neural networks, this work uses the simplest and the most widely used type of network, i.e. Feed Forward Neural Network. In this type of a network, the information moves in only one direction, forward, i.e. from the input nodes, through the hidden nodes (if any) and to the output nodes. There are two main types of feed forward neural networks, a single layer and multi layer. A single-layer network (Figure 5.1) is a network which consists of a single layer of nodes; the inputs are fed directly to the outputs via a series of weights. In this way it can be considered the simplest kind of feed-forward network. The (McCulloch-Pitts) perceptron (Figure 5.2) is a single layer NN with a non-linear sign function. The sum of the products of the weights and the inputs is calculated in each node, and if the value is above a predetermined threshold (typically 0) the neuron is activated and takes the activated value; otherwise it takes the deactivated value. The multilayer feed-forward neural network (Figure 5.3) consists of multiple layers of computational units, usually interconnected in a feed- forward way. Each neuron in one layer has directed connections to the neurons of the following layer. 141
Figure 5.2 McCulloch-Pitts (Meyer-Baese, 2009). Figure 5.3 Multi- Layer Feed Forward Network (Meyer-Baese, 2009). 5.2. Previous work Almasri & Kaluarachchi (2005) have shown that Modular Neural Networks (MNN) can be used to predict the nitrate distribution in groundwater using the on-ground nitrogen loading and recharge data. In order to study the distribution of nitrates in the Sumas- Blaine aquifer in Whatcom County (Washington State), they created a MNN and compared the results to that of a classical fate and transport model. Their results indicate that the perfromance of the MNN is better than the ANN, and that the MNN can provide 142
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THE FLORIDA STATE UNIVERSITY ARTS A
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To my Dad, Mum and Brother iii
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Dr. Stephen Kish for his words of e
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1.5. Evaluation of methods to estim
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2.5.8. Texture 8 (Silt Loam).......
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4.2.6. Texture 6 (Sandy Clay Loam).
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LIST OF TABLES Table 1.1 Additional
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LIST OF FIGURES Figure 1.1 Oxidatio
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Figure 2.51 8-20-84; Denitrificatio
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Figure 3.6 Probability plot (Textur
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Three statistical methods were used
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• Microbial biomass/ plant uptake
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Figure 1.1 Oxidation of organic car
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possibility of denitrification (Smi
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50ºC (36ºF - 122ºF) (Brady & Wei
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1.2.6. Salinity Salinity is a known
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1.3.1. The Acetylene inhibition met
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Among the variety of successful met
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a first-order decay process. The ma
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look at the data shows that it fail
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Da is the denitrification rate (mg
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a simple linear regression of organ
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of the annual N2O emission and deni
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easons for the limitation of the mo
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Table 1.2 Continued dC / dt Organic
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also implies that the transferabili
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CHAPTER TWO 2. LINEAR REGRESSION An
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an improvement on the earlier attem
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2.3. Texture Table 2.1 Soil Textura
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2.3.5. Texture 5 (Sand) The surfici
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2.3.10. Texture 10 (Silty Clay Loam
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Textural Class Table 2.2 Coefficien
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R_d_n (kgN ha-1 d-1) R_d_n (kgN ha-
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2.4.3. Texture 3 (Loam) Texture 3 c
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R_d_n (kgN ha-1 d-1) Texture 3 Temp
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R_d_n (kgN ha-1 d-1) 6 5 4 3 2 1 0
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2.4.7. Texture 7 (Sandy Loam) The S
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Texture 7 Temperature 12 : Denitrif
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Rdn (kgN ha-1 d-1) Texture 9 Temper
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Rdn (kgN ha-1 d-1) 16 12 8 4 0 Text
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2.5. Break down by Texture, Tempera
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8-20-34 (n=3), 8-20-84 (n=4), 8-20-
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Subsets 9-7-100, 9-25-100 and 9-30-
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Texture 10 Temperature 25 WFP 100 :
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a significant linear relationship b
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Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
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C9 Rdn (kgN ha-1 d-1) 12 10 8 6 4 2
Page 111 and 112: Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
Page 113 and 114: Rdn (kgN ha-1 d-1) Texture 10 Tempe
Page 115 and 116: 2.6.11. Texture 11 (Silt) No data a
Page 117 and 118: 2.7.6. Texture 6 (Sandy Clay Loam)
Page 119 and 120: Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
Page 121 and 122: Rdn (kgN ha-1 d-1) Texture 8 Tepera
Page 123 and 124: Rdn (kgN ha-1 d-1) Texture 8 Tepera
Page 125 and 126: 2.8. Summary The correlation coeffi
Page 127 and 128: where, M is the slope of the linear
Page 129 and 130: Hence for the first set of equation
Page 131 and 132: 3.2.2. Texture-Temperature-WFP-pH T
Page 133 and 134: Organic Carbon (%) Actual Rdn Gener
Page 135 and 136: R dn Results C = 9.389 - 3.210* M -
Page 137 and 138: The actual denitrification values f
Page 139 and 140: Eigenvalue 1.5 1.4 1.3 1.2 1.1 1.0
Page 141 and 142: R indicated by the significant code
Page 143 and 144: R Table 4.5 Comparison of actual an
Page 145 and 146: Table 4.8 Texture 2, Comparison of
Page 147 and 148: Table 4.11 Comparison of actual and
Page 149 and 150: The two equations are applied to th
Page 151 and 152: R R dn dn = - 0.05 ∗Temperature (
Page 153 and 154: Table 4.19 Texture 8, Linear multi-
Page 155 and 156: Table 4.20 Continued Actual Rdn Pre
Page 157 and 158: Once again the equations developed
Page 159 and 160: 4.2.11. Texture 11 (Silt) No data a
Page 161: CHAPTER FIVE 5. ANALYSIS USING NEUR
Page 165 and 166: developed. In order to control over
Page 167 and 168: Eventually the final set of network
Page 169 and 170: 5.4.4. Texture 5 (Sand) Figure 5.5
Page 171 and 172: 5.4.6. Texture 8 (Silt Loam) Figure
Page 173 and 174: 5.4.8. Texture 10 (Silty Clay Loam)
Page 175 and 176: CHAPTER SIX 6. USE OF ISOTOPES TO E
Page 177 and 178: 18 ⎛ ⎛ ⎜ O ⎞ 16 ⎜ ⎜ ⎟
Page 179 and 180: Figure 6.1 Estimation of Nitrate Lo
Page 181 and 182: δ 18 O 12 10 8 6 4 2 Table 6.1 Egg
Page 183 and 184: δ 18 O 20.00 18.00 16.00 14.00 12.
Page 185 and 186: CHAPTER SEVEN 7. APPLICATION TO JAC
Page 187 and 188: 7.2. Multiple Regression Three equa
Page 189 and 190: Table 7.4 Multi-Regression and Neur
Page 191 and 192: 8.3. Main Results The linear equati
Page 193 and 194: isotopes can be particularly useful
Page 195 and 196: APPENDIX B CONVERSION SHEET FOR DEN
Page 197 and 198: iv. v. vi. Given in the dataset: 1.
Page 199 and 200: Conversion from −3 −1 g N m d t
Page 201 and 202: Convert From Multiply by Convert to
Page 203 and 204: Texture 4 Variable Count N* Mean St
Page 205 and 206: Texture 10 Variable Count N* Mean S
Page 207 and 208: Texture-Temperature-WFP-pH Code Equ
Page 209 and 210: 09-15-25-09 Rdn = 0.0106688*OC + 0.
Page 211 and 212: Code Rdn - OC Code Rdn-WFP Code Rdn
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- Code Rdn - OC Code Rdn - pH Code
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- Code Rdn - OC Code Rdn - NO3 Conc
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Code Rdn - OC Code Rdn - pH 08-25-9
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REFERENCES Almasri, M. N., & Kaluar
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Fellows, C., Hunter, H., Eccleston,
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King, D., & Nedwell, D. B. (1985).
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Ozden, T., & Muhammetoglu, H. (2008
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Smith, R., Bohlke, J., Garabedian,
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Zhu, J., Liu, G., Han, Y., Zhang, Y
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