About the Authors
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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. – 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 , 7–8 disadvantages , 320–321 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 , 79–80, 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, 9–16 System (AGROSIM) Aquil , 93 advantages , 267 Arti fi cial bee colony (ABC) algorithm crop selection , 263, application of , 22, 23 disadvantages , 267–268 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 , 80–83 264–265 onlooker bees , 27 costing section , 265 population-based search algorithm , 22 meteorological parameter section , 264 probability analysis , 27–28 rainfall input fi eld , 266 ranking system , 24–27 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 , 181–183 performance metrics , 69 extreme event prediction , 108 predators and competitive species , groundwater quality 72–73 ( 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 , 61–62 Bahaj, A.S. , 190 neural network , 63–64 Barkhatov, N.A. , 93 objective function , 63 Barten , 219 species of , 60 Bat clusterization variables control toad population , 62–63 clusterization and identi fi cation Burlando, P. , 46 ef fi ciency , 153 computational capacity , 153–154 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 , 152–154 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 , 241–243 Bernauer, T. , 79 correct classi fi cation rate , 241 Bernoulli’s equation , 194 encoded category , 238, 240 Blunden, L.S. , 190 objective function, , 238–239 Bodri, L. , 49, 50 rating , 238–239 Boix, C. , 164 Climate-Optimized Basic fuzzy-Algo for Boyd , 220 identi fi cation of Location for Tidal Brenner , 220 power (COBALT) , 196–197, 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 , 59–60 method , 143, 144 factors affecting growth rate , 62 data segmentation , 140–142 GA de fi nition , 236 advantages , 60 environmental factors , 139 AHP , 65 fuzzy clusterization, application , 150, 151 application , 65–67 GIS and remote sensing , 139 climate change scenario , 69–71 hierarchical , 140 de fi nition of , 64–65 k-means , 140–143 environmental sustainability , 71 neuro-fuzzy , 237 growth rate function , 68 principal component analysis , 143 input variable categorization and signi fi cance of , 234–235 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 , 170–171 Correlation gap percentage (CGP) , 209 Rudrasagar lake , 168, 174 Couch, S.J. , 190 Sipahijala reservoir , 168, 174 Cyr, J.F. , 139, 141, tourist paradise , 164–165 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 7–8 causes , 318–320 feasibility study , 17 de fi nition , 318–319 food-search logic , 17 impacts , 319 pheromone marker , 5 urbanization and industry growth , 319 probability analysis , 17