About the Authors

Principal Authors:

Dr. Mrinmoy Majumder presently works as Assistant Professor in the School of Hydro-Informatics Engineering at the National Institute of Technology, Agartala, India. He received his Ph.D. in 2010 from Jadavpur University. Presently he teaches and conducts research on hydroinformatics, natural resource management, and nature-based algorithms. He has published more than 25 papers in national and international journals and has written three books. Address : 43/6/1/1, Jheelroad, Kolkata-700031, West Bengal, India. Email : [email protected] Dr. Rabindra Nath Barman presently works as Assistant Professor in the Department of Production Engineering at the National Institute of Technology, Agartala, India. He received his Ph.D. from Jadavpur University in 2012. He is interested in fl uid mechanics and water resource principles. He has published more than 25 papers in national and international journals and has written several book chapters. Address : Department of Production Engineering, National Institute of Technology Agartala, Barjala, Jirania 799055, Tripura, India. Email : [email protected]

Coauthors:

Miss Tilottoma Chackraborty Tilottoma Chackraborty presently works as Assistant Professor in the School of Hydro-Informatics Engineering at the National Institute of Technology Agartala, India. She completed her M. Tech in 2010 in the School of Water Resource Engineering, Jadavpur University. She currently teaches and conducts research on hydraulic structures, water resource management, and hydrologic modeling. She has published several book chapters.

M. Majumder and R.N. Barman (eds.), Application of Nature Based Algorithm 331 in Natural Resource Management, DOI 10.1007/978-94-007-5152-1, © Springer Science+Business Media Dordrecht 2013 332 About the Authors

Address: School of Hydro-Informatics Engineering, National Institute of Technology Agartala, Barjala, Jirania 799055, Tripura, India. Email : [email protected] Miss Paulami De presently works as Senior Research Fellow in a project funded by the Department of Science and Technology, Government of India, and was accepted into the School of Hydro-Informatics Engineering in the National Institute of Technology, Agartala, India. She completed her M. Tech in 2011 in the School of Water Resource Engineering, Jadavpur University. She is currently engaged in research on waste and surface water treatment mechanisms. Address: School of Hydro-Informatics Engineering, National Institute of Technology Agartala, Barjala, Jirania 799055, Tripura, India. Email : [email protected] Mr. Soumya Ghosh presently works as a Lecturer at the JAYPEE Institute of Technology and Management, India. He completed his Master of Technology at the School of Water Resource Engineering, Jadavpur University, in 2010. He currently teaches and conducts research on renewable energy, tidal power, and electrical machines. Address: School of Hydro-Informatics Engineering, National Institute of Technology Agartala, Barjala, Jirania 799055, Tripura, India. Email : [email protected] Mr. Bipal Kr . Jana presently works in consulting engineering services in Kolkata, India. He has published more than ten papers in various national and international journals and has written one book in the fi eld of environmental science and carbon sequestration. Address: Consulting Engineering Services, “AKARIK”, East Tentulberia, P.O. Ð Panchpota, (Near Five Star Club), Kolkata 700152, West Bengal, India. Email : [email protected] Dr. Debasri Roy presently works as Associate Professor in the School of Water Resource Engineering, Jadavpur University, India. She currently teaches and conducts research in the fi eld of water resource and hydrology engineering. She has published more than 15 papers in national and international journals and has supervised more than three Ph.D. candidates. Address: School of Water Resources Engineering, Jadavpur University, Kolkata 700032, West Bengal, India. Email : [email protected] Index

A Agroforestry, 320 ABC algorithm. See Arti fi cial bee colony Akbari, S. , 91 (ABC) algorithm Alcamo, J. , 79 Abdullayev, N.V. , 91 Altunkaynak, A. , 111 Abundo, M. , 190 Alvisi, S. , 49 ACO algorithm. See Ant colony optimization ANNs. See Arti fi cial neural (ACO) algorithm networks (ANNs) Afforestation Ant colony optimization (ACO) algorithm bene fi ts , 320 application of , 5, 6 carbon sequestration , 318 changing topology , 5 de fi nition , 318 decision-making parameter hierarchy , 7Ð8 disadvantages , 320Ð321 ecopark (see Ecopark) geophysical and climatic attributes, 321, 322 feasibility study , 17 neuro-fuzzy model (see Neuro-fuzzy food-search logic , 17 system) nature-based algorithm , 79Ð80, 82 vs. reforestation , 318 pheromone marker , 5 socioeconomic attributes , 321, 323 probability analysis , 7 species selection , 321 site-selection process , 5 Afshar, M.H. , 6 tertiary factor, categorization and ranking , Agriculture Optimization and Simulation 7, 9Ð16 System (AGROSIM) Aquil , 93 advantages , 267 Arti fi cial bee colony (ABC) algorithm crop selection , 263, application of , 22, 23 disadvantages , 267Ð268 combinatorial data matrix , 29 help interface , 262, 264 exploration and exploitation process , 23 logical and pro fi table cultivation , 261 food searching logic , 23 optimization interface , 262, 264, 266 honey bee, food-search logic , 22 simulation interface , 262, 264 initialization , 27 additional cost and output sections , nature-based algorithm , 80Ð83 264Ð265 onlooker bees , 27 costing section , 265 population-based search algorithm , 22 meteorological parameter section , 264 probability analysis , 27Ð28 rainfall input fi eld , 266 ranking system , 24Ð27 types of plant section , 264 scout bees , 28 urbanization impact , 266 variables, hospitable area selection , 22 water productivity index, , 263 worker bees , 27

M. Majumder et al., Application of Nature Based Algorithm 333 in Natural Resource Management, DOI 10.1007/978-94-007-5152-1, © Springer Science+Business Media Dordrecht 2013 334 Index

Arti fi cial neural networks (ANNs) large-scale urbanization , 72 and ACO models , 83, 84 parameters of , 68, 69 applications , 181Ð183 performance metrics , 69 extreme event prediction , 108 predators and competitive species , groundwater quality 72Ð73 ( see Groundwater quality) scenarios affecting toad population , irrigation canals , 222, 223 72, 73 LM-trained ANN model, 327, 328 training algorithm , 69 water availability prediction , 79 types of , 65 Austin , 221 growth rate prediction , 62 input variable categorization and scoring , 63 B life cycle , 61Ð62 Bahaj, A.S. , 190 neural network , 63Ð64 Barkhatov, N.A. , 93 objective function , 63 Barten , 219 species of , 60 Bat clusterization variables control toad population , 62Ð63 clusterization and identi fi cation Burlando, P. , 46 ef fi ciency , 153 computational capacity , 153Ð154 food spotting , 152 C vs. fuzzy clusterization , 153 Cao, K. , 124 good and bad location, food availability , 152 Cay, T. , 325 microbats, echo location , 146 Čermák, V. , 49, 50 pulse emission and loudness rate , 150 Ceyhun, Ö. , 182 search optimization algorithm , 150 Chatterjee , 160, 167 weighted average , 152Ð154 Chau, K.W. , 92, 223 Bayramoglu, M. , 323 Chaves, P. , 182, 223 Bazartseren, B. , 182 Chikumbo, O. , 67 Beck, L. , 79 Cimen, M. , 45 Beniston, M. , 164 Climate from Image (CLIMAGE) software , 237 Benlarbia, K. , 110 bene fi ts , 241Ð243 Bernauer, T. , 79 correct classi fi cation rate , 241 Bernoulli’s equation , 194 encoded category , 238, 240 Blunden, L.S. , 190 objective function, , 238Ð239 Bodri, L. , 49, 50 rating , 238Ð239 Boix, C. , 164 Climate-Optimized Basic fuzzy-Algo for Boyd , 220 identi fi cation of Location for Tidal Brenner , 220 power (COBALT) , 196Ð197, 200 Briceño-Elizondo, E. , 163 Cluster analysis (clustering) Bryden, I.G. , 190 bat clusterization (see Bat clusterization) Bufo melanostictus clusterization model and clustering ecological role , 59Ð60 method , 143, 144 factors affecting growth rate , 62 data segmentation , 140Ð142 GA de fi nition , 236 advantages , 60 environmental factors , 139 AHP , 65 fuzzy clusterization, application , 150, 151 application , 65Ð67 GIS and remote sensing , 139 climate change scenario , 69Ð71 hierarchical , 140 de fi nition of , 64Ð65 k-means , 140Ð143 environmental sustainability , 71 neuro-fuzzy , 237 growth rate function , 68 principal component analysis , 143 input variable categorization and signi fi cance of , 234Ð235 ranking , 65, 68 site selection , 143 Index 335

Cognitive indexes. See Irrigation canals fl ora and fauna , 166 Cohen, S.J. , 164 Global Change in Biomass study , 175 Coillie, F.M.B.V. , 67 Gumti reservoir , 167, 173 Common gradient descent (CGD) , 215, 216 image processing fl owchart , 169, 170 Cooper , 221 land use area , 170Ð171 Correlation gap percentage (CGP) , 209 Rudrasagar lake , 168, 174 Couch, S.J. , 190 Sipahijala reservoir , 168, 174 Cyr, J.F. , 139, 141, tourist paradise , 164Ð165 Trishna reservoir , 168, 175 wetland watershed , 173 D wetland deterioration and ecosystem DCS. See Distributed control system (DCS) quality , 161 Decision tree algorithm (DTA) , 224,, Ecopark 229, 230 ACO algorithm Defne, Z. , 190 application of , 5, 6 Deforestation , 319 changing topology , 5 Deng, W. , 141 decision-making parameter hierarchy , Deserti fi cation 7Ð8 causes , 318Ð320 feasibility study , 17 de fi nition , 318Ð319 food-search logic , 17 impacts , 319 pheromone marker , 5 urbanization and industry growth , 319 probability analysis , 17 vegetation , 319, 320 site-selection process , 5 watershed management , 319 tertiary factor, categorization and Distributed control system (DCS) , 315 ranking , 7, 9Ð16 Dorigo, M. , 17, 79 de fi nition of , 4 Duan, H. , 24 ecotourism de fi nition and principle , 4 Dudhani, S. , 142 site selection , 4Ð5 Duin, R.P.W. , 90 Erethizon dorsatum . See Porcupine Eroğlu, Y. , 6

E Ebb tide , 249 F Ecological sensitivity, wetlands Fahmy, A.A. , 24 aquatic ecosystem, stress , 163 Falcone , 221 climate change impacts , 163, 164 Fang, Y. , 141 decision tool algorithm , 163Ð164 Filho, A.J.P. , 223 ecology and ecosystem , 161Ð162 Filtration , 271 environmental deterioration affect , 160 Firat, M. , 110 remote sensing , 160 Flocculation , 271 saline emergent wetland habitat , 162Ð163 Flood tide , 249 sustainability , 160 Foody, G.M. , 325 tidal and nontidal wetland habitat , 162 Franchini, M. , 49 Tripura , 166 French, M.N. , 46 agriculture , 165Ð166 biodiversity hotspot , 165 biome , 175 G classes and climate scenario , GA. See Genetic algorithm (GA) 172, 173 Gaafar, L.K. , 66 classi fi cation rule , 171 Galán, C.O. , 66 climatic impact prediction , 172 Gallagher , 206 data collection , 169 Ganesan, T. , 123 decision tree mechanism , 172 Gautam, A.R. , 49 degradation of , 167 Gautam, M.R. , 223 336 Index

Genetic algorithm (GA) Honda, K. , 66 advantages , 60 Hou, Z. , 6 AHP , 65 HPP. See Hydropower plant (HPP) application of , 65Ð67, 122, 124Ð125 HS. See Hydrologic sensitivity (HS) climate change scenario , 69Ð71 Hsieh, T.-J. , 24 crop selection, vertical irrigation system , 88 Hu, J. , 6 de fi nition of , 64Ð65 Hussain, M.A. , 91 environmental sustainability , 71 Huwe, B. , 111 growth rate function , 68 Hydro energy , 248 input variable categorization and ranking , Hydrologic sensitivity (HS) 65, 68 HS index , 180 large-scale urbanization , 72 prediction of , 187 lotus cultivation , 122, 126 SBR (see Sundarban Biosphere Reserve parameters of , 68, 69 (SBR)) performance metrics , 69 Hydropower plant (HPP) predators and competitive species , 72Ð73 classi fi cation of , 37Ð38, 137Ð139 scenarios affecting toad population , 72, 73 climate change, impact , 33 training algorithm , 69 cluster analysis algorithm types of , 65 bat clusterization (see Bat water availability prediction , 79 clusterization) Genta, J.L. , 142 clusterization model and clustering Ghadimi, A.A. , 139, 141, method , 143, 144 Golmohammadi, D. , 91 data segmentation , 140Ð142 Groundwater quality environmental factors , 139 arti fi cial neural networks , 209 fuzzy clusterization, application , network topology selection , 213 150, 151 CGP , 209 GIS and remote sensing , 139 Cl and pH , 215 hierarchical clustering , 140 Damodar River , 207, 208 k-means clustering , 140Ð143 data description , 208Ð209 principal component analysis , 143 GA search settings , 215 site selection , 143 objective and scope, 207 decision-making process , 140 Panchet and Maithon reservoir , 207, 208 energy production , 135Ð136 performance metrics , 214Ð215 environmental and ecological sensitivity variation ranking , 139 in chloride , 210 environmental factor , 148Ð149 in conductivity , 210 environment and ecological balance , 35Ð36 in pH , 210, 211 factor selection , 146 in total hardness , 210, 211 GIS and remote sensing , 139 in turbidity , 210 high-head HPP , 33 Guided neuroclustering methods (GNCM) hydrologic and geophysical factor , cluster weights , 226, 228, 230 147Ð148 and DTA, , 230 Indian scenario, energy distribution, Gupta, L. , 93 136Ð137 load factor , 31 location selection , 136 H low-head HPP , 32 Ha, H. , 182 medium-head HPP, 33 Hajeka , 218 power production capacity , 32 Hammar, L. , 190 primary energy global consumption , 36 He , 221 PSO (see Particle swarm optimization) Heathwaite , 220 renewable energy, global scenario , 36Ð37 He, J. , 6 socioeconomic factor , 149Ð150 Holland, J.H. , 64 tradeoff zone , 33 Index 337

utilization factor , 32 Kumari, S. , 91 world population and economic Kusre, B.C. , 139, 141, 145 development , 35

L I LaMeres, B.J. , 110 Iliadis, L. , 326 Larentis, D.G. , 139, 142 Index of biotic integrity (IBI) , 220 Lekouch, K. , 48 Ines, A.V.M. , 66, 125 LevenbergÐMarquardt (LM) , 214Ð216, 327 INSAT satellite Kalpana , 243 Lime softening , 271Ð272 Intergovernmental Panel on Climate Change Linguistic fuzzy modeling , 323 (IPCC) , 106 Liu, Z. , 92 categories , 84 Lohani, A.K. , 92 SBR , 185Ð187 Lotus cultivation Irrigation canals climate change scenario , 129 annual variation of fl ow , 224 cultivation methodology, problems , 120 arti fi cial neural network , 222, 223 GA (see Genetic algorithm) buffer ponds , 225 neurogenetic model , 122 channel loss , 224 pond selection , 120 clusterization of training data , 227 species and root function , 119Ð120 decision tree algorithm, , 224 uses of , 120 demand , 225 GNCM , 226, 228Ð230 groundwater contribution , 225 M optimal con fi guration , 230 Mafakheri, E. , 91 sedimentation , 225 Maity, D. , 123 storage capacity , 224 Majhi, B. , 91 volume of fl ow , 224 Mamdani model , 323 Iscan, F. , 325 Manzato, A. , 50 Ismaylova, K.S. , 91 Martos, J.C. , 223 IUCN Conservation Status Ranking , 59 McAvoy. M. , 93 Mean square error (MSE) , 214, 215 Merino, G. , 164 J Meteorological parameter section (MPS) , 264 Jacobs , 221 Mettam, C. , 190 Jain, A. , 49 Mondok , 220 Jain, B.A. , 90 Monjezi, M. , 123 Jalali-Farahani, F. , 6 Mougiakakou, S.G. , 223 Jang, J. S. R. , 323 Moustra, M. , 92 MSE. See Mean square error (MSE)

K Kamel, M.S. , 6 N Karabogaa, D. , 111 Nag, B.N. , 90 Karaboga, D. , 22Ð24, 80 Nature-based algorithm Kardani-Moghaddam, S. , 66 ABC algorithm , 80, 82Ð83 Karthika , 220 ACO algorithm , 79Ð80, 82 Khalid, A. , 111 ANN , 79 Khashei, M. , 48 CCR , 81 Kim, J.-W. , 49 climate change and water availability , Kisi, O. , 45, 49 78Ð79 Kojiri, T. , 182, 223 conceptual and statistical model , 77 Kottegoda, N.T. , 45 data set preparation , 80Ð81 Kumar, A.M. , 49 ef fi cacy, conceptual model , 84 338 Index

Nature-based algorithm (cont.) objective function development , 113 GA , 79 objective model, variables , 107 IPCC A2 and B2 scenario , 83, 84 and performance metrics , 100, 101, 113, 116 kappa index and CCR , 84 prediction accuracy , 106 methodology , 80 rank and degree of importance , 94, 99 performance metrics , 83 ranking, fuzzy matrix , 113Ð115 root relative square error , 81 real-time setting, model ef fi ciency , 113 water availability prediction , 78 rice and maize suitability , 101Ð102 Nehrir, M.H. , 109 rule matrix and membership function , Nelumbo nucifera . See Lotus 94, 100 Neuro-fuzzy system scoring mechanism, development , 110Ð113 application , 324Ð326 step-by-step description, crop suitability , de fi nition , 323 94Ð98 hybridization , 323 suitability function , 101, 102 input parameter topology selection , 108 degree of ratings , 324, 327 training algorithm , 108 objective function value , 324 trial-and-error method , 106 performance metrics , 327, 3287 water-related problem, fuzzy logic , 110, 111 rank , 324, 327, 328 weighted average , 88Ð89 weight , 327, 328 zone of certainty prediction , 110 input variables , 321 Neurogenetic algorithm interpretability vs. accuracy , 323 application of , 122, 123 linguistic fuzzy modeling , 323 climate change scenario , 129 LM algorithm , 327 combinatorial data matrix , 122 neural network model , 327 data matrix , 51 objective , 321 data preprocessing , 51, 53 output variable , 323 data set categorization , 53Ð54 precise fuzzy modeling , 324 data set rating and categorization , species suitability , 327, 329, 330 126Ð128 Neuro-fuzzy technique geophysical characteristics , 130 ANN , 105Ð106 input and output variables , 51Ð53, 121 application of , 89, 92Ð93 LM algorithm , 129 Atrazine, advective fl ux , 110 model decision, pond suitability , 133 categorization fuzzy theory , 107 network topology , 55 combinatorial data matrix , 89 nonlinear relationship mapping , 122 crop selection , 88 optimal weighting , 121 data set parameter characteristics , 128, 129 preparation , 110Ð112 parameter characteristics and performance variable categorization method , metrics , 51, 54 99, 100 performance metrics , 55 decision support system , 102 pond chemical properties , 130 defuzzi fi cation procedure , 94 population and climate change , 133 demand-side management , 109 QP and CGD , 55 environ-metrics , 110 stochastic neural network and STR , 44, 50 extreme event prediction , 106 suitability function , 128 factors, crop cultivation , 87Ð88 suitability prediction, input variable , fuzzy logic theory , 89, 108Ð109 130Ð133 GA , 89 toned-down situation , 130 land scarcity , 87 training algorithm , 54 maximization rule theory , 94 variable categorization , 51, 53 network topology and activation Neurogenetic models. See Groundwater function , 90 quality neural network model , 90Ð91 Nicholas, I. , 67 neural network parameters , 113, 116 Nunes, V. , 142 Index 339

O convergence of , 40Ð41 Ocampo-Duquea, W. , 111 data matrix , 40 Oh, H.J. , 325 iteration technique , 35 Olsson, J. , 50 objective function , 38Ð39 Ona , 111 rainfall data and power demand , 38 Ooka, R. , 66 social, population-based search OPTIDAL software algorithm , 34 disadvantages , 259Ð260 SSHP planning , 41 energy ef fi ciency , 259 variants of , 35 environmentalists , 259 water balance and power equation , 39 fl ow turbulence ,253 Passino, K.M. , 24 government , 259 Philippart, C.J.M. , 163 index , 256 Piotrowski, A.P. , 48 input Porcupine climatic , 252 ABC algorithm (see Arti fi cial bee colony data entry window , 251 (ABC) algorithm) ecological , 251, 253 and ecosystem , 20 electrical , 251, 254 food habit , 21 fl ow turbulence , 253 habitat , 21 geophysical , 251, 252 predators , 21 head-difference fi eld , 253 selection methodology, habitat , 20 home panel , 251, 254 uses of , 20 location selector and optimizer , 251 Pradhan, B. , 325 socioeconomic , 251, 253 Precise fuzzy modeling , 324 watershed loss and channel loss , 252 PSO. See Particle swarm optimization location identi fi cation , 258Ð259 (PSO) net pro fi t optimization , 258 objective and scope , 251 output panel Q graphical , 257 QUALTR software location optimizer , 256, 257 advantages , 315 location selector panel , 256 disadvantages , 315Ð316 optimization result , 256, 258 Microsoft Excel functions , 315 power generation, input variables , 259 neurogenetic models production unit cascading , 272, 273 explicit costs , 254 categories and ratings , 273, 307Ð314 fi xed costs , 255 combinatorial data matrix , 273 implicit costs , 255 input and output , 273Ð306 variable costs , 255 objective and scope , 272 Optimization interface , 262, 263, 266 Spreadsheet Converter 6, 315 Oryza sp. , 101, 102 standalone Javascript program , 315 Ou, S.-L. , 124 Quick propagation (QP) , 313 Overgrazing , 319 Quijano, N. , 24 Ozturk, C. , 24

R P Rajagopalan, N. , 91 Pachepsky, Y.A. , 49 Rajendran, C. , 6 Panagopoulos, Y. , 125 Raju, K.S. , 223 Pan, T.-y. , 50 Razavi, S. , 6 Papalexiou, S.-M. , 45 Reay , 206 Parish, E.S. , 79 Revunov, S.E. , 93 Particle swarm optimization (PSO) Rodríguez, J.A. , 223 application of , 35 Rojanamon, P. , 139, 142, 145 340 Index

S network training parameters , 185 Sahoo, B. , 123 objective and scope , 180 Sand fi lter , 271 QPNNEA model , 185 Santos, C.C. , 223 training algorithms , 183 Saruwatari, N. , 111 Supriyasilp, T. , 142 Satellite imagery , 235Ð236, 243, 244 Surface water quality. See Groundwater quality SBR. See Sundarban Biosphere Reserve (SBR) Schobe, A. , 93 Schulz, K. , 111 T Seçkiner, S.U. , 6 Tachos, S., 326 Sen, Z. , 111 Taheri, J. , 24 Shaw , 221 Takagi-Sugeno-Kang (TSK) model , 324 Sheng , 220 Teegavarapu, R.S.V. , 93 Shiri, J. , 125 Thermal energy , 249 Short-range weather forecasting (SRWF) , Thielen, J. , 46 234Ð236 Tidal energy Short-term rainfall (STR) fl ood tide and ebb tide , 249 electricity demand , 44 OPTIDAL (see OPTIDAL software) estimation of , 44Ð46 optimization , 250Ð251 neural network, application , 47Ð49 tidal power stations , 250 neurogenetic algorithm (see Neurogenetic Tidal power plants algorithm) COBALT formulation , 196Ð197 prediction of , 43 environmental factors , 190 probability and magnitude , 47, 51 estimation of , 193 stochastic neural network , 47, 50 factors , 191 WTP, 44 low-cost interconnection point , 195 Simulation interface , 262, 264 net pro fi t determination , 195 additional cost and output sections , 265 normal and changed climate scenarios, costing section, 265 197Ð200 meteorological parameter section , 264 physical factors , 190 rainfall input fi eld , 266 reservoir level , 192 types of plant section , 264 site selection, 190Ð191 urbanization impact , 266 socioeconomic factors , 190 Singh, T.N. , 123 stream resources determination , 193Ð194 Şişman-Yilmaz, N.A., 326 study objective and scope , 191 Space Application Center (SAC) , 166 Sundarbans, wildlife sanctuaries , 192 Spring water treatment plant , 271 turbulence determination , 194Ð195 Srivastava, G. , 325 Tomandl, D. , 93 Stagnitti , 221 Torfs, P., 223 Stenstrom, M.K. , 182 Tripura wetland STR. See Short-term rainfall (STR) agriculture , 165Ð166 Sugimoto, S. , 45 biodiversity hotspot , 165 Sundarban Biosphere Reserve (SBR) biome and climate interrelation , 175 activation function , 184 classi fi cation rule , 171 ANN , 181Ð183 climatic impact prediction , 172 CGDNNGA model , 185 data collection , 169 fi tness functions , 184 decision tree mechanism , 172 fl ora and fauna , 178Ð179 degradation of, wetlands , 167 freshwater availability , 180 ecological sensitivity classes and climate hydroclimatic conditions of , 179Ð180 scenario , 172, 173 IPCC scenarios , 185Ð187 fl ora and fauna , 166 model parameters and performance Global Change in Biomass study , 175 metrics , 185 Gumti reservoir , 167, 173 network topology , 183 image processing fl owchart , 169, 170 Index 341

land use area , 170Ð171 Watershed management, 89, 206, 319 Rudrasagar lake , 168, 174 Water treatment plants (WTPs) Sipahijala reservoir , 168, 174 climate impacts, 272 tourist paradise , 164Ð165 components , 271Ð272 Trishna reservoir , 168, 175 drinking water , 270 wetland watershed , 173 hazard analysis , 270Ð271 QUALTR (see QUALTR software) Wei, C.C. , 91 V Wójcik, R. , 223 Variable costs , 255 WTPs. See Water treatment plants (WTPs) Verstraeten , 206 Wu, C.L. , 92, 223 Vertical irrigation system WWTPs. See Waste water treatment plants combinatorial data matrix , 89 (WWTPs) crop selection , 88 factors, crop cultivation, 88 land scarcity , 87 X neuro-fuzzy technique (see Neuro-fuzzy Xia, L. , 91 technique) Xin-She Yang , 150 rice and maize suitability , 101,102 Xiong, Y. , 182 weighted average , 89 Xu , C, 24

W Y Wang , 221 Yalçin, A. , 182 Wang, G. , 163 Yamaguchi, T. , 93 Wang, J. , 6 Yang, X.H., 223 Wang, R.-y. , 50 Yang, Y. , 6 Wanqa , 220 Yan-hua, Z. , 6 Waste water treatment plants (WWTPs), Yi, C.S. , 139, 141, 145 270Ð272 Yildirim, Y. , 325 Waszczyszyn, Z. , 91 Yomota, A. , 111 Water productivity Yoon, H. , 223 AGROSIM (see Agriculture Optimization Yun, R. , 125 and Simulation System (AGROSIM)) Yurduseva, M.A. , 110 crop selection , 263, index , 263, pro fi t and water requirements, 263 Z pro fi t estimation , 263 Zadeh , L, 109