Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms
Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Townshi...
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| Published in | The Science of the total environment Vol. 615; pp. 438 - 451 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
15.02.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0048-9697 1879-1026 1879-1026 |
| DOI | 10.1016/j.scitotenv.2017.09.262 |
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| Abstract | Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses.
[Display omitted]
•The performance of meta-heuristics was assessed in flood susceptibility mapping.•ANFIS-PSO adopted faster convergence algorithm and outperformed other models.•ANFIS-PSO showed practical and robust results compared to other models. |
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| AbstractList | Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses. Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses. [Display omitted] •The performance of meta-heuristics was assessed in flood susceptibility mapping.•ANFIS-PSO adopted faster convergence algorithm and outperformed other models.•ANFIS-PSO showed practical and robust results compared to other models. Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses.Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood management are necessary in order to reduce its harmful effects. The aim of the present study is to map flood hazard over the Jahrom Township in Fars Province using a combination of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristics algorithms such as ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO) and comparing their accuracy. A total number of 53 flood locations areas were identified, 35 locations of which were randomly selected in order to model flood susceptibility and the remaining 16 locations were used to validate the models. Learning vector quantization (LVQ), as one of the supervised neural network methods, was employed in order to estimate factors' importance. Nine flood conditioning factors namely: slope degree, plan curvature, altitude, topographic wetness index (TWI), stream power index (SPI), distance from river, land use/land cover, rainfall, and lithology were selected and the corresponding maps were prepared in ArcGIS. The frequency ratio (FR) model was used to assign weights to each class within particular controlling factor, then the weights was transferred into MATLAB software for further analyses and to combine with metaheuristic models. The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes. The chi-square result attests the same, where the ANFIS-PSO had the highest spatial differentiation within flood susceptibility classes over the study area. The area under the curve (AUC) obtained from ROC curve indicated the accuracy of 91.4%, 91.8%, 92.6% and 94.5% for the respective models of FR, ANFIS-ACO, ANFIS-GA, and ANFIS-PSO ensembles. So, the ensemble of ANFIS-PSO was introduced as the premier model in the study area. Furthermore, LVQ results revealed that slope degree, rainfall, and altitude were the most effective factors. As regards the premier model, a total area of 44.74% was recognized as highly susceptible to flooding. The results of this study can be used as a platform for better land use planning in order to manage the highly susceptible zones to flooding and reduce the anticipated losses. |
| Author | Razavi Termeh, Seyed Vahid Pourghasemi, Hamid Reza Kornejady, Aiding Keesstra, Saskia |
| Author_xml | – sequence: 1 givenname: Seyed Vahid surname: Razavi Termeh fullname: Razavi Termeh, Seyed Vahid organization: Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran – sequence: 2 givenname: Aiding surname: Kornejady fullname: Kornejady, Aiding organization: Department of Watershed Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran – sequence: 3 givenname: Hamid Reza orcidid: 0000-0003-2328-2998 surname: Pourghasemi fullname: Pourghasemi, Hamid Reza email: hr.pourghasemi@shirazu.ac.ir organization: Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran – sequence: 4 givenname: Saskia surname: Keesstra fullname: Keesstra, Saskia email: saskia.keesstra@wur.nl organization: Soil Physics and Land Management Group, Wageningen University, Droevendaalsesteeg 4, 6708PB Wageningen, Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28988080$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.catena.2017.01.010 10.1016/j.procs.2015.04.212 10.1007/s11069-011-9879-4 10.1016/S0304-3800(00)00322-7 10.1007/s00477-010-0436-6 10.1086/587826 10.1016/j.geoderma.2017.06.020 10.1016/j.jhydrol.2010.12.027 10.1016/j.agrformet.2016.11.002 10.1007/s11053-007-9043-8 10.1007/s12665-009-0394-9 10.1016/S0022-5193(05)80686-1 10.1108/09653560610659775 10.1016/j.catena.2012.05.005 10.1007/s11269-017-1589-6 10.1016/0893-6080(90)90071-R 10.1016/j.cageo.2011.10.031 10.1007/s00254-007-0788-5 10.3390/ijgi5100191 10.1061/(ASCE)HE.1943-5584.0000040 10.1016/j.jhydrol.2009.06.005 10.1016/j.catena.2017.05.034 10.1007/s12665-016-5424-9 10.1007/s11069-011-9831-7 10.1016/j.medengphy.2016.07.003 10.1109/4235.985692 10.1002/hyp.3360050103 10.1016/j.tcs.2005.05.020 10.1007/s10661-015-5049-6 10.1007/s12665-014-3289-3 10.1016/j.jhydrol.2011.10.010 10.1016/j.eswa.2010.12.167 10.1007/s00477-015-1021-9 10.1007/s12665-016-5774-3 10.1007/s11069-013-0932-3 10.1016/j.scitotenv.2015.08.055 10.1007/s10661-016-5665-9 10.1016/S0167-8809(01)00187-6 10.1016/j.dsp.2006.05.001 10.4028/www.scientific.net/AMM.225.486 10.2475/ajs.290.5.569 10.1080/10106049.2015.1041559 10.1007/s11069-012-0217-2 10.1007/s12665-011-1504-z 10.1016/j.geomorph.2015.10.030 10.1016/j.eswa.2011.02.108 10.1007/s11069-013-0728-5 10.1007/s12665-015-4950-1 10.5194/hess-15-617-2011 10.1016/j.jhydrol.2013.09.034 10.1016/j.jhydrol.2009.09.037 10.1016/j.jhydrol.2009.12.013 10.1016/j.geomorph.2017.09.007 10.1016/j.geomorph.2009.02.026 10.1016/j.eswa.2007.06.026 10.1080/10106049.2014.966161 10.1007/s13762-013-0464-0 10.1016/j.jhydrol.2012.09.006 10.1016/j.cageo.2012.08.023 10.1016/j.applthermaleng.2004.06.024 10.1080/02626667909491834 10.14207/ejsd.2012.v1n2p85 10.1016/j.jhydrol.2014.02.053 10.1007/s11069-007-9197-z 10.1016/j.scitotenv.2015.02.027 10.1007/s11069-016-2357-2 10.1016/j.catena.2017.07.002 10.1007/s12517-012-0825-x 10.1016/j.scitotenv.2017.07.198 |
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| References | Pourghasemi, Beheshtirad (bb0335) 2014; 30 Polat, Gunes (bb0320) 2006; 16 Pontius, Schneider (bb0330) 2001; 85 Saito, Nakayama, Matsuyama (bb0420) 2009; 109 Tehrany, Pradhan, Jebur (bb0460) 2015; 29 Mahapatra, Daniel, Dey, Nayak (bb0245) 2015; 48 Sivanandam, Deepa (bb0440) 2007 Lee, Kang, Jeon (bb0230) 2012 Kohonen (bb0200) 1995 Ravagnani, Silva, Arroyo, Constantino (bb0410) 2005; 25 Pourghasemi, Pradhan, Gokceoglu (bb0355) 2012; 63 Tunusluoglu, Gokceoglu, Nefeslioglu, Sonmez (bb9100) 2008; 54 Lin, Xia, Jiang, Bai, Wu (bb0235) 2016; 5 Yu, Ding, Zhu (bb0500) 2011; 38 Kenndy, Eberhart (bb0175) 1995; 4 Oh, Kim, Choi, Park, Lee (bb0295) 2011; 399 Pradhan (bb0380) 2010; 9 Khosravi, Nohani, Maroufinia, Pourghasemi (bb0190) 2016; 83 Pourghasemi, Mohammady, Pradhan (bb0345) 2012; 97 Agyare (bb0010) 2004; 17 Beven, Kirkby (bb0040) 1979; 24 Bui, Bui, Nguyen, Pradhan, Nampak, Trinh (bb0060) 2017; 233 Ahalt, Krishnamurthy, Chen, Melton (bb0015) 1990; 3 Cloke, Pappenberger (bb0110) 2009; 375 Pradhan (bb0385) 2013; 51 Zabihi, Pourghasemi, Pourtaghi, Behzadfar (bb0510) 2016; 75 Srivastava, Denis, Srivastava, Kumar, Kumar (bb0445) 2014; 3 Pourtaghi, Pourghasemi (bb0375) 2014; 22 Kornejady, Ownegh, Rahmati, Bahremand (bb0225) 2017 Naghibi, Pourghasemi, Dixon (bb0270) 2016; 188 Kohonen, Hynninen, Kangas, Laaksonen, Torkkola (bb0205) 1996 Chen, Pourghasemi, Kornejady, Zhang (bb0085) 2017; 305 Dorigo, Blum (bb0135) 2005; 344 Wu, Lien, Chang (bb0485) 2010; 24 Khaledian, Brevik, Pereira, Cerdà, Fattah, Tazikeh (bb0180) 2017; 158 Yilmaz (bb0495) 2010; 61 Kornejady, Ownegh, Bahremand (bb0220) 2017; 152 Poli, Sterlacchini (bb0325) 2007; 16 Kornejady, Kohzad, Sarparast, Khosravi, Mombeini (bb0210) 2014; 4 Mukerji, Chatterjee, Raghuwanshi (bb0265) 2009; 14 Pourghasemi, Pradhan, Gokceoglu (bb0350) 2012; 225 Kazakis, Kougias, Patsialis (bb0170) 2015; 538 Kornejady, Heidari, Nakhavali (bb0215) 2015; 3 Cherqui, Belmeziti, Granger, Sourdril, Le Gauffre (bb0095) 2015; 514 Messner, Meyer (bb0250) 2006 Ozdemir (bb0305) 2011; 411 Chen, Pourghasemi, Panahi, Kornejady, Wang, Xie, Cao (bb0090) 2017; 297 Pourghasemi, Yousefi, Kornejady, Cerdà (bb0370) 2017; 609 Norouzi, Taslimi (bb0285) 2012; 12 Sezer, Pradhan, Gokceoglu (bb0425) 2011; 38 Nourani, Pradhan, Ghaffari, Sharifi (bb0290) 2014; 71 Negnevitsky (bb0280) 2002; 394 Central Office of Natural Resources and Watershed Management in the Jahrom Township (CONRWMJT) (bb0065) 2015; 1 Olden, Lawler, Poff (bb0300) 2008; 83 Jung, Chang, Moradkhani (bb0165) 2011; 15 Tiwari, Chatterjee (bb0475) 2010; 382 Clerc, Kennedy (bb0105) 2002; 6 Tehrany, Pradhan, Jebur (bb0450) 2013; 504 Khosravi, Pourghasemi, Chapi, Bahri (bb0185) 2016; 188 Hussin, Zumpano, Reichenbach, Sterlacchini, Micu, van Westen, Bălteanu (bb0150) 2016; 253 Iranian Department of Natural Resources Management [IDNRM] (bb0155) 2002 Armaş (bb0030) 2012; 60 Doocy, Daniels, Packer, Dick, Kirsch (bb0130) 2013; 5 Huang, Tan, Zhou, Yang, Benjamin, Wen, Fen (bb0145) 2008; 47 Pourghasemi, Moradi, Aghda, Gokceoglu, Pradhan (bb0365) 2014; 7 Mathur, Glesk, Buis (bb9000) 2016; 38 Miller (bb0255) 1990; 290 Pourghasemi, Kerle (bb0340) 2016; 75 Adeli, Hung (bb0005) 1994 Chau, Wu, Li (bb0070) 2005; 10 Pearce, Ferrier (bb0310) 2000; 133 Moore, Grayson, Ladson (bb0260) 1991; 5 Tehrany, Lee, Pradhan, Jebur, Lee (bb0455) 2014; 72 Beckers, Deneubourg, Goss (bb0035) 1992; 159 Jaafari, Najafi, Pourghasemi, Rezaeian, Sattarian (bb0160) 2014; 11 Nampak, Pradhan, Manap (bb0275) 2014; 513 Sahoo, Schladow, Reuter (bb0415) 2009; 378 Rahmati, Pourghasemi (bb0400) 2017; 31 Akgün, Bulut (bb0020) 2007; 51 Rahmati, Pourghasemi, Zeinivand (bb0405) 2016; 31 Pierdicca, Pulvirenti, Chini, Guerriero, Ferrazzoli (bb0315) 2010 Yesilnacar (bb0490) 2005 Clapcott, Goodwin, Snelder (bb0100) 2013; 2301 Pourghasemi, Moradi, Aghda (bb0360) 2013; 69 Degiorgis, Gnecco, Gorni, Roth, Sanguineti, Taramasso (bb0125) 2012; 470 Singhal, Goyal (bb0435) 2012; 1 Chen, Panahi, Pourghasemi (bb0080) 2017; 157 Kia, Pirasteh, Pradhan, Mahmud, Sulaiman, Moradi (bb0195) 2012; 67 Bonham-Carter (bb0050) 1994; 13 Sheta, Turabieh (bb0430) 2006; 6 Tien Bui, Pradhan, Lofman, Revhaug, Dick (bb0465) 2012; 45 Billa, Shattri, Mahmud, Ghazali (bb0045) 2006; 15 Chen, Yeh, Yu (bb0075) 2011; 59 Qian, Chen, Xiang, Zhang, Niu (bb0395) 2016; 75 Wang, Elhag (bb0480) 2008; 34 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0345) 2012; 97 Sezer (10.1016/j.scitotenv.2017.09.262_bb0425) 2011; 38 Khaledian (10.1016/j.scitotenv.2017.09.262_bb0180) 2017; 158 Sahoo (10.1016/j.scitotenv.2017.09.262_bb0415) 2009; 378 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0365) 2014; 7 Ravagnani (10.1016/j.scitotenv.2017.09.262_bb0410) 2005; 25 Agyare (10.1016/j.scitotenv.2017.09.262_bb0010) 2004; 17 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0370) 2017; 609 Tien Bui (10.1016/j.scitotenv.2017.09.262_bb0465) 2012; 45 Iranian Department of Natural Resources Management [IDNRM] (10.1016/j.scitotenv.2017.09.262_bb0155) Wang (10.1016/j.scitotenv.2017.09.262_bb0480) 2008; 34 Degiorgis (10.1016/j.scitotenv.2017.09.262_bb0125) 2012; 470 Kia (10.1016/j.scitotenv.2017.09.262_bb0195) 2012; 67 Mahapatra (10.1016/j.scitotenv.2017.09.262_bb0245) 2015; 48 Pourtaghi (10.1016/j.scitotenv.2017.09.262_bb0375) 2014; 22 Armaş (10.1016/j.scitotenv.2017.09.262_bb0030) 2012; 60 Jaafari (10.1016/j.scitotenv.2017.09.262_bb0160) 2014; 11 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0335) 2014; 30 Billa (10.1016/j.scitotenv.2017.09.262_bb0045) 2006; 15 Polat (10.1016/j.scitotenv.2017.09.262_bb0320) 2006; 16 Yu (10.1016/j.scitotenv.2017.09.262_bb0500) 2011; 38 Bui (10.1016/j.scitotenv.2017.09.262_bb0060) 2017; 233 Ozdemir (10.1016/j.scitotenv.2017.09.262_bb0305) 2011; 411 Beven (10.1016/j.scitotenv.2017.09.262_bb0040) 1979; 24 Kohonen (10.1016/j.scitotenv.2017.09.262_bb0205) 1996 Zabihi (10.1016/j.scitotenv.2017.09.262_bb0510) 2016; 75 Oh (10.1016/j.scitotenv.2017.09.262_bb0295) 2011; 399 Tunusluoglu (10.1016/j.scitotenv.2017.09.262_bb9100) 2008; 54 Clerc (10.1016/j.scitotenv.2017.09.262_bb0105) 2002; 6 Negnevitsky (10.1016/j.scitotenv.2017.09.262_bb0280) 2002; 394 Pierdicca (10.1016/j.scitotenv.2017.09.262_bb0315) 2010 Norouzi (10.1016/j.scitotenv.2017.09.262_bb0285) 2012; 12 Pradhan (10.1016/j.scitotenv.2017.09.262_bb0385) 2013; 51 Jung (10.1016/j.scitotenv.2017.09.262_bb0165) 2011; 15 Moore (10.1016/j.scitotenv.2017.09.262_bb0260) 1991; 5 Chen (10.1016/j.scitotenv.2017.09.262_bb0075) 2011; 59 Pearce (10.1016/j.scitotenv.2017.09.262_bb0310) 2000; 133 Chau (10.1016/j.scitotenv.2017.09.262_bb0070) 2005; 10 Chen (10.1016/j.scitotenv.2017.09.262_bb0080) 2017; 157 Lin (10.1016/j.scitotenv.2017.09.262_bb0235) 2016; 5 Kornejady (10.1016/j.scitotenv.2017.09.262_bb0220) 2017; 152 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0350) 2012; 225 Cherqui (10.1016/j.scitotenv.2017.09.262_bb0095) 2015; 514 Srivastava (10.1016/j.scitotenv.2017.09.262_bb0445) 2014; 3 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0355) 2012; 63 Pontius (10.1016/j.scitotenv.2017.09.262_bb0330) 2001; 85 Tehrany (10.1016/j.scitotenv.2017.09.262_bb0460) 2015; 29 Huang (10.1016/j.scitotenv.2017.09.262_bb0145) 2008; 47 Singhal (10.1016/j.scitotenv.2017.09.262_bb0435) 2012; 1 Khosravi (10.1016/j.scitotenv.2017.09.262_bb0185) 2016; 188 Kazakis (10.1016/j.scitotenv.2017.09.262_bb0170) 2015; 538 Hussin (10.1016/j.scitotenv.2017.09.262_bb0150) 2016; 253 Dorigo (10.1016/j.scitotenv.2017.09.262_bb0135) 2005; 344 Central Office of Natural Resources and Watershed Management in the Jahrom Township (CONRWMJT) (10.1016/j.scitotenv.2017.09.262_bb0065) 2015; 1 Clapcott (10.1016/j.scitotenv.2017.09.262_bb0100) 2013; 2301 Beckers (10.1016/j.scitotenv.2017.09.262_bb0035) 1992; 159 Naghibi (10.1016/j.scitotenv.2017.09.262_bb0270) 2016; 188 Poli (10.1016/j.scitotenv.2017.09.262_bb0325) 2007; 16 Cloke (10.1016/j.scitotenv.2017.09.262_bb0110) 2009; 375 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0340) 2016; 75 Doocy (10.1016/j.scitotenv.2017.09.262_bb0130) 2013; 5 Kornejady (10.1016/j.scitotenv.2017.09.262_bb0215) 2015; 3 Lee (10.1016/j.scitotenv.2017.09.262_bb0230) 2012 Kornejady (10.1016/j.scitotenv.2017.09.262_bb0210) 2014; 4 Mathur (10.1016/j.scitotenv.2017.09.262_bb9000) 2016; 38 Rahmati (10.1016/j.scitotenv.2017.09.262_bb0405) 2016; 31 Qian (10.1016/j.scitotenv.2017.09.262_bb0395) 2016; 75 Saito (10.1016/j.scitotenv.2017.09.262_bb0420) 2009; 109 Nampak (10.1016/j.scitotenv.2017.09.262_bb0275) 2014; 513 Pradhan (10.1016/j.scitotenv.2017.09.262_bb0380) 2010; 9 Khosravi (10.1016/j.scitotenv.2017.09.262_bb0190) 2016; 83 Olden (10.1016/j.scitotenv.2017.09.262_bb0300) 2008; 83 Kenndy (10.1016/j.scitotenv.2017.09.262_bb0175) 1995; 4 Adeli (10.1016/j.scitotenv.2017.09.262_bb0005) 1994 Akgün (10.1016/j.scitotenv.2017.09.262_bb0020) 2007; 51 Pourghasemi (10.1016/j.scitotenv.2017.09.262_bb0360) 2013; 69 Mukerji (10.1016/j.scitotenv.2017.09.262_bb0265) 2009; 14 Nourani (10.1016/j.scitotenv.2017.09.262_bb0290) 2014; 71 Chen (10.1016/j.scitotenv.2017.09.262_bb0090) 2017; 297 Kohonen (10.1016/j.scitotenv.2017.09.262_bb0200) 1995 Miller (10.1016/j.scitotenv.2017.09.262_bb0255) 1990; 290 Ahalt (10.1016/j.scitotenv.2017.09.262_bb0015) 1990; 3 Tiwari (10.1016/j.scitotenv.2017.09.262_bb0475) 2010; 382 Rahmati (10.1016/j.scitotenv.2017.09.262_bb0400) 2017; 31 Sheta (10.1016/j.scitotenv.2017.09.262_bb0430) 2006; 6 Yilmaz (10.1016/j.scitotenv.2017.09.262_bb0495) 2010; 61 Chen (10.1016/j.scitotenv.2017.09.262_bb0085) 2017; 305 Bonham-Carter (10.1016/j.scitotenv.2017.09.262_bb0050) 1994; 13 Tehrany (10.1016/j.scitotenv.2017.09.262_bb0450) 2013; 504 Tehrany (10.1016/j.scitotenv.2017.09.262_bb0455) 2014; 72 Yesilnacar (10.1016/j.scitotenv.2017.09.262_bb0490) 2005 Messner (10.1016/j.scitotenv.2017.09.262_bb0250) 2006 Sivanandam (10.1016/j.scitotenv.2017.09.262_bb0440) 2007 Kornejady (10.1016/j.scitotenv.2017.09.262_bb0225) 2017 Wu (10.1016/j.scitotenv.2017.09.262_bb0485) 2010; 24 |
| References_xml | – volume: 375 start-page: 613 year: 2009 end-page: 626 ident: bb0110 article-title: Ensemble flood forecasting: a review publication-title: J. Hydrol. – volume: 14 start-page: 647 year: 2009 end-page: 652 ident: bb0265 article-title: Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models publication-title: J. Hydrol. Eng. – volume: 9 start-page: 1 year: 2010 end-page: 18 ident: bb0380 article-title: Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing publication-title: J. Spat. Hydrol. – volume: 34 start-page: 3099 year: 2008 end-page: 3106 ident: bb0480 article-title: An adaptive neuro-fuzzy inference system for bridge risk assessment publication-title: Expert Syst. Appl. – volume: 3 start-page: 71 year: 2014 end-page: 79 ident: bb0445 article-title: Morphometric analysis of a Semi Urban Watershed, trans Yamuna, draining at Allahabad using Cartosat (DEM) data and GIS publication-title: Int. J. Eng. Sci. – volume: 399 start-page: 158 year: 2011 end-page: 172 ident: bb0295 article-title: GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea publication-title: J. Hydrol. – volume: 470 start-page: 302 year: 2012 end-page: 315 ident: bb0125 article-title: Classifiers for the detection of flood-prone areas using remote sensed elevation data publication-title: J. Hydrol. – volume: 344 start-page: 243 year: 2005 end-page: 278 ident: bb0135 article-title: Ant colony optimization theory: a survey publication-title: Theor. Comput. Sci. – volume: 51 start-page: 1377 year: 2007 end-page: 1387 ident: bb0020 article-title: GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region publication-title: Environ. Lithol. – year: 2002 ident: bb0155 article-title: PRINCIPLES of Watershed Management and Land Use Planning – volume: 67 start-page: 251 year: 2012 end-page: 264 ident: bb0195 article-title: An artificial neural network model for flood simulation using GIS: Johor River Basin Malaysia publication-title: Environ. Earth Sci. – volume: 7 start-page: 1857 year: 2014 end-page: 1878 ident: bb0365 article-title: GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran) publication-title: Arab. J. Geosci. – volume: 51 start-page: 350 year: 2013 end-page: 365 ident: bb0385 article-title: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS publication-title: Comput. Geosci. – start-page: 432 year: 2005 ident: bb0490 article-title: The Application of Computational Inelegance to Landslide Susceptibility Mapping in Turkey – year: 1994 ident: bb0005 article-title: Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems – volume: 83 start-page: 947 year: 2016 end-page: 987 ident: bb0190 article-title: A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique publication-title: Nat. Hazards – volume: 71 start-page: 523 year: 2014 end-page: 547 ident: bb0290 article-title: Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models publication-title: Nat. Hazards – volume: 152 start-page: 144 year: 2017 end-page: 162 ident: bb0220 article-title: Landslide susceptibility assessment using maximum entropy model with two different data sampling methods publication-title: Catena – volume: 225 start-page: 486 year: 2012 end-page: 491 ident: bb0350 article-title: Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon's entropy and GIS publication-title: Appl. Mech. Mater. – volume: 75 start-page: 1 year: 2016 end-page: 16 ident: bb0395 article-title: A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China publication-title: Environ. Earth Sci. – volume: 59 start-page: 1261 year: 2011 end-page: 1276 ident: bb0075 article-title: Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan publication-title: Nat. Hazards – volume: 3 start-page: 85 year: 2015 end-page: 109 ident: bb0215 article-title: Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran publication-title: Environ. Resour. Res. – volume: 133 start-page: 225 year: 2000 end-page: 245 ident: bb0310 article-title: Evaluating the predictive performance of habitat models developed using logistic regression publication-title: Ecol. Model. – volume: 157 start-page: 310 year: 2017 end-page: 324 ident: bb0080 article-title: Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling publication-title: Catena – volume: 16 start-page: 71 year: 2006 end-page: 84 ident: bb0320 article-title: A hybrid medical decision making system based on principle component analysis, k-NN based weighted pre-processing and adaptive neuro fuzzy inference system publication-title: Digital Signal Process. – volume: 54 start-page: 9 year: 2008 end-page: 22 ident: bb9100 article-title: Extraction of potential debris source areas by logistic regression technique: A case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey) publication-title: Environ. Geol. – volume: 60 start-page: 937 year: 2012 end-page: 950 ident: bb0030 article-title: Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania publication-title: Nat. Hazards – volume: 47 start-page: 65 year: 2008 end-page: 73 ident: bb0145 article-title: Flood hazard in Hunan province of China: an economic loss analysis publication-title: Nat. Hazards – volume: 253 start-page: 508 year: 2016 end-page: 523 ident: bb0150 article-title: Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model publication-title: Geomorphology – start-page: 175 year: 1995 end-page: 189 ident: bb0200 article-title: Learning Vector Quantization; Self-organizing Maps – volume: 188 start-page: 44 year: 2016 ident: bb0270 article-title: GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran publication-title: Environ. Monit. Assess. – volume: 2301 start-page: 35 year: 2013 ident: bb0100 article-title: Predictive Models of Benthic Macro-invertebrate Metrics. Cawthron Report – volume: 538 start-page: 555 year: 2015 end-page: 563 ident: bb0170 article-title: Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: application in Rhodope–Evros region, Greece publication-title: Sci. Total Environ. – volume: 16 start-page: 121 year: 2007 end-page: 134 ident: bb0325 article-title: Landslide representation strategies in susceptibility studies using weights-of-evidence modeling technique publication-title: Nat. Resour. Res. – volume: 75 start-page: 1 year: 2016 end-page: 17 ident: bb0340 article-title: Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran publication-title: Environ. Earth Sci. – start-page: 1991 year: 1996 end-page: 1992 ident: bb0205 article-title: LVQ PAK: the learning vector quantization program package publication-title: Technical report, Laboratory of Computer and Information Science Rakentajanaukio 2 C – start-page: 4796 year: 2010 end-page: 4798 ident: bb0315 article-title: A fuzzy-logic-based approach for flood detection from Cosmo-SkyMed data publication-title: Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE Int. – volume: 38 start-page: 1083 year: 2016 end-page: 1089 ident: bb9000 article-title: Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. publication-title: Med. Eng. Phys. – volume: 30 start-page: 662 year: 2014 end-page: 685 ident: bb0335 article-title: Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran publication-title: Geocarto Int. – volume: 69 start-page: 749 year: 2013 end-page: 779 ident: bb0360 article-title: Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances publication-title: Nat. Hazards – volume: 3 start-page: 277 year: 1990 end-page: 290 ident: bb0015 article-title: Competitive learning algorithms for vector quantization publication-title: Neural Netw. – volume: 233 start-page: 32 year: 2017 end-page: 44 ident: bb0060 article-title: A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area publication-title: Agric. For. Meteorol. – volume: 394 year: 2002 ident: bb0280 article-title: Artificial Intelligence: A Guide to Intelligent Systems – volume: 5 start-page: 3 year: 1991 end-page: 30 ident: bb0260 article-title: Digital terrain modelling: a review of hydrological, geomorphological, and biological applications publication-title: Hydrol. Process. – volume: 290 start-page: 569 year: 1990 end-page: 599 ident: bb0255 article-title: Morphometric assessment of lithologic controls on drainage basin evolution in the Crawford upland, south-central Indiana publication-title: Am. J. Sci. – volume: 1 start-page: 121 year: 2015 end-page: 122 ident: bb0065 article-title: Hydrology and Flood Technical Report – volume: 61 start-page: 821 year: 2010 end-page: 836 ident: bb0495 article-title: Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine publication-title: Environ. Earth Sci. – volume: 4 start-page: 169 year: 2014 end-page: 176 ident: bb0210 article-title: Performance assessment of two “LNRF” and “AHP-Area Density” models in landslide susceptibility zonation publication-title: J. Life Sci. Biomed. – volume: 158 start-page: 194 year: 2017 end-page: 200 ident: bb0180 article-title: Modeling soil cation exchange capacity in multiple countries publication-title: Catena – volume: 5 start-page: 191 year: 2016 ident: bb0235 article-title: Landslide susceptibility mapping based on particle swarm optimization of multiple kernel relevance vector machines: case of a low hill area in Sichuan Province, China publication-title: ISPRS Int. J. Geo-Inf. – volume: 513 start-page: 283 year: 2014 end-page: 300 ident: bb0275 article-title: Application of GIS based data driven evidential belief function model to predict groundwater potential zonation publication-title: J. Hydrol. – volume: 25 start-page: 1223-1217 year: 2005 ident: bb0410 article-title: Heat exchanger network synthesis and optimization using genetic algorithm publication-title: Appl. Therm. Eng. – volume: 45 start-page: 199 year: 2012 end-page: 211 ident: bb0465 article-title: Landslide susceptibility mapping at Hoa Binh Province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS publication-title: Comput. Geosci. – volume: 382 start-page: 20 year: 2010 end-page: 33 ident: bb0475 article-title: Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) publication-title: J. Hydrol. – volume: 10 start-page: 485 year: 2005 end-page: 491 ident: bb0070 article-title: Comparison of several flood forecasting models in Yangtze River publication-title: J. Hydraul. Eng. – volume: 12 start-page: 921 year: 2012 end-page: 926 ident: bb0285 article-title: The impact of flood damages on production of Iran's Agricultural Sector. Middle East publication-title: J. Sci. Res. – volume: 22 start-page: 643 year: 2014 end-page: 662 ident: bb0375 article-title: GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran publication-title: Hydrolith. J. – volume: 17 year: 2004 ident: bb0010 article-title: Soil Characterization and Modeling of Spatial Distribution of Saturated Hydraulic Conductivity at Two Sites in the Volta Basin of Ghana – volume: 83 start-page: 171 year: 2008 end-page: 193 ident: bb0300 article-title: Machine learning without tears: a primer for ecologists publication-title: Q. Rev. Biol. – volume: 48 start-page: 753 year: 2015 end-page: 768 ident: bb0245 article-title: Induction motor control using PSO-ANFIS publication-title: Procedia Comput. Sci. – volume: 13 start-page: 398 year: 1994 ident: bb0050 article-title: Geographic information systems for geoscientists-modeling with GIS publication-title: Comput. Methods Geosci. – volume: 31 start-page: 1473 year: 2017 end-page: 1487 ident: bb0400 article-title: Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models publication-title: Water Resour. Manag. – volume: 188 start-page: 656 year: 2016 ident: bb0185 article-title: Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon's entropy, statistical index, and weighting factor models publication-title: Environ. Model. Assess. – volume: 24 start-page: 1175 year: 2010 end-page: 1191 ident: bb0485 article-title: Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation publication-title: Stoch. Env. Res. Risk A. – start-page: 1 year: 2017 end-page: 68 ident: bb0225 article-title: Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND publication-title: Geocarto Int. – start-page: 895 year: 2012 end-page: 898 ident: bb0230 article-title: Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS publication-title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich – volume: 159 start-page: 397 year: 1992 end-page: 415 ident: bb0035 article-title: Trails and U-turns in the selection of the shortest path by the ant Lasius niger publication-title: J. Theor. Biol. – year: 2007 ident: bb0440 article-title: Introduction to Genetic Algorithms – volume: 504 start-page: 69 year: 2013 end-page: 79 ident: bb0450 article-title: Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS publication-title: J. Hydrol. – volume: 378 start-page: 325 year: 2009 end-page: 342 ident: bb0415 article-title: Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models publication-title: J. Hydrol. – volume: 15 start-page: 617 year: 2011 end-page: 633 ident: bb0165 article-title: Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects publication-title: Hydrol. Earth Syst. Sci. – volume: 411 start-page: 290 year: 2011 end-page: 308 ident: bb0305 article-title: GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison publication-title: J. Hydrol. – volume: 305 start-page: 314 year: 2017 end-page: 327 ident: bb0085 article-title: Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques publication-title: Geoderma – volume: 29 start-page: 1149 year: 2015 end-page: 1165 ident: bb0460 article-title: Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method publication-title: Stoch. Env. Res. Risk A. – volume: 63 start-page: 965 year: 2012 end-page: 996 ident: bb0355 article-title: Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran publication-title: Nat. Hazards – volume: 4 start-page: 1942 year: 1995 end-page: 1948 ident: bb0175 article-title: Particle Swarm Optimization. In Proceedings of IEEE International Conference on neural networks – volume: 72 start-page: 4001 year: 2014 end-page: 4015 ident: bb0455 article-title: Flood susceptibility mapping using integrated bivariate and multivariate statistical models publication-title: Environ. Earth Sci. – volume: 85 start-page: 239 year: 2001 end-page: 248 ident: bb0330 article-title: Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA publication-title: Agric. Ecosyst. Environ. – volume: 6 start-page: 67 year: 2006 end-page: 74 ident: bb0430 article-title: A comparison between genetic algorithms and sequential quadratic programming in solving constrained optimization problems publication-title: ICGST Int. J. Artif. Intell. Mach. Lear. – volume: 38 start-page: 10568 year: 2011 end-page: 10573 ident: bb0500 article-title: A hybrid GA–TS algorithm for open vehicle routing optimization of coal mines material publication-title: Expert Syst. Appl. – volume: 38 start-page: 8208 year: 2011 end-page: 8219 ident: bb0425 article-title: Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang Valley Malaysia publication-title: Expert Syst. Appl. – volume: 609 start-page: 764 year: 2017 end-page: 775 ident: bb0370 article-title: Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling publication-title: Sci. Total Environ. – volume: 297 start-page: 69 year: 2017 end-page: 85 ident: bb0090 article-title: Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques publication-title: Geomorphology – volume: 31 start-page: 42 year: 2016 end-page: 70 ident: bb0405 article-title: Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golestan Province, Iran publication-title: Geocarto Int. – volume: 75 start-page: 665 year: 2016 ident: bb0510 article-title: GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran publication-title: Environ. Earth Sci. – volume: 24 start-page: 43 year: 1979 end-page: 69 ident: bb0040 article-title: A physically based, variable contributing area model of basin hydrology publication-title: Hydrol. Sci. Bull. – volume: 109 start-page: 108 year: 2009 end-page: 121 ident: bb0420 article-title: Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains Japan publication-title: Geomorphology – volume: 11 start-page: 909 year: 2014 end-page: 926 ident: bb0160 article-title: GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran publication-title: Int. J. Environ. Sci. Technol. – volume: 15 start-page: 233 year: 2006 end-page: 240 ident: bb0045 article-title: Comprehensive planning and the role of SDSS in flood disaster management in Malaysia publication-title: Disaster Prev Manag – volume: 5 year: 2013 ident: bb0130 article-title: The human impact of earthquakes: a historical review of events 1980–2009 and systematic literature review publication-title: PLoS Curr. – volume: 514 start-page: 418 year: 2015 end-page: 425 ident: bb0095 article-title: Assessing urban potential flooding risk and identifying effective risk-reduction measures publication-title: Sci. Total Environ. – volume: 97 start-page: 71 year: 2012 end-page: 84 ident: bb0345 article-title: Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran publication-title: Catena – volume: 1 start-page: 85 year: 2012 ident: bb0435 article-title: A methodology based on spatial distribution of parameters for understanding affect of rainfall and vegetation density on groundwater recharge publication-title: Eur. J. Sustain. Dev. – volume: 6 start-page: 58 year: 2002 end-page: 73 ident: bb0105 article-title: The particle swarm-explosion, stability and convergence in a multidimensional complex space publication-title: IEEE Trans. Evol. Comput. – start-page: 149 year: 2006 end-page: 167 ident: bb0250 article-title: Flood damage, vulnerability and risk perception–challenges for flood damage research – volume: 152 start-page: 144 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0220 article-title: Landslide susceptibility assessment using maximum entropy model with two different data sampling methods publication-title: Catena doi: 10.1016/j.catena.2017.01.010 – volume: 48 start-page: 753 year: 2015 ident: 10.1016/j.scitotenv.2017.09.262_bb0245 article-title: Induction motor control using PSO-ANFIS publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.04.212 – start-page: 175 year: 1995 ident: 10.1016/j.scitotenv.2017.09.262_bb0200 – volume: 60 start-page: 937 issue: 3 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0030 article-title: Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania publication-title: Nat. Hazards doi: 10.1007/s11069-011-9879-4 – volume: 1 start-page: 121 year: 2015 ident: 10.1016/j.scitotenv.2017.09.262_bb0065 – volume: 133 start-page: 225 issue: 3 year: 2000 ident: 10.1016/j.scitotenv.2017.09.262_bb0310 article-title: Evaluating the predictive performance of habitat models developed using logistic regression publication-title: Ecol. Model. doi: 10.1016/S0304-3800(00)00322-7 – volume: 24 start-page: 1175 issue: 8 year: 2010 ident: 10.1016/j.scitotenv.2017.09.262_bb0485 article-title: Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-010-0436-6 – year: 1994 ident: 10.1016/j.scitotenv.2017.09.262_bb0005 – start-page: 1 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0225 article-title: Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND publication-title: Geocarto Int. – volume: 83 start-page: 171 issue: 2 year: 2008 ident: 10.1016/j.scitotenv.2017.09.262_bb0300 article-title: Machine learning without tears: a primer for ecologists publication-title: Q. Rev. Biol. doi: 10.1086/587826 – volume: 305 start-page: 314 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0085 article-title: Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques publication-title: Geoderma doi: 10.1016/j.geoderma.2017.06.020 – volume: 399 start-page: 158 issue: 3 year: 2011 ident: 10.1016/j.scitotenv.2017.09.262_bb0295 article-title: GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2010.12.027 – volume: 13 start-page: 398 year: 1994 ident: 10.1016/j.scitotenv.2017.09.262_bb0050 article-title: Geographic information systems for geoscientists-modeling with GIS publication-title: Comput. Methods Geosci. – start-page: 149 year: 2006 ident: 10.1016/j.scitotenv.2017.09.262_bb0250 – volume: 51 start-page: 1377 issue: 8 year: 2007 ident: 10.1016/j.scitotenv.2017.09.262_bb0020 article-title: GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region publication-title: Environ. Lithol. – volume: 233 start-page: 32 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0060 article-title: A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2016.11.002 – volume: 16 start-page: 121 issue: 2 year: 2007 ident: 10.1016/j.scitotenv.2017.09.262_bb0325 article-title: Landslide representation strategies in susceptibility studies using weights-of-evidence modeling technique publication-title: Nat. Resour. Res. doi: 10.1007/s11053-007-9043-8 – volume: 61 start-page: 821 issue: 4 year: 2010 ident: 10.1016/j.scitotenv.2017.09.262_bb0495 article-title: Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine publication-title: Environ. Earth Sci. doi: 10.1007/s12665-009-0394-9 – volume: 159 start-page: 397 year: 1992 ident: 10.1016/j.scitotenv.2017.09.262_bb0035 article-title: Trails and U-turns in the selection of the shortest path by the ant Lasius niger publication-title: J. Theor. Biol. doi: 10.1016/S0022-5193(05)80686-1 – volume: 15 start-page: 233 year: 2006 ident: 10.1016/j.scitotenv.2017.09.262_bb0045 article-title: Comprehensive planning and the role of SDSS in flood disaster management in Malaysia publication-title: Disaster Prev Manag doi: 10.1108/09653560610659775 – volume: 97 start-page: 71 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0345 article-title: Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran publication-title: Catena doi: 10.1016/j.catena.2012.05.005 – volume: 31 start-page: 1473 issue: 5 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0400 article-title: Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models publication-title: Water Resour. Manag. doi: 10.1007/s11269-017-1589-6 – volume: 3 start-page: 277 issue: 3 year: 1990 ident: 10.1016/j.scitotenv.2017.09.262_bb0015 article-title: Competitive learning algorithms for vector quantization publication-title: Neural Netw. doi: 10.1016/0893-6080(90)90071-R – volume: 45 start-page: 199 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0465 article-title: Landslide susceptibility mapping at Hoa Binh Province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2011.10.031 – volume: 5 year: 2013 ident: 10.1016/j.scitotenv.2017.09.262_bb0130 article-title: The human impact of earthquakes: a historical review of events 1980–2009 and systematic literature review publication-title: PLoS Curr. – volume: 54 start-page: 9 issue: 1 year: 2008 ident: 10.1016/j.scitotenv.2017.09.262_bb9100 article-title: Extraction of potential debris source areas by logistic regression technique: A case study from Barla, Besparmak and Kapi mountains (NW Taurids, Turkey) publication-title: Environ. Geol. doi: 10.1007/s00254-007-0788-5 – volume: 5 start-page: 191 issue: 10 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0235 article-title: Landslide susceptibility mapping based on particle swarm optimization of multiple kernel relevance vector machines: case of a low hill area in Sichuan Province, China publication-title: ISPRS Int. J. Geo-Inf. doi: 10.3390/ijgi5100191 – volume: 9 start-page: 1 issue: 2 year: 2010 ident: 10.1016/j.scitotenv.2017.09.262_bb0380 article-title: Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing publication-title: J. Spat. Hydrol. – volume: 14 start-page: 647 issue: 6 year: 2009 ident: 10.1016/j.scitotenv.2017.09.262_bb0265 article-title: Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models publication-title: J. Hydrol. Eng. doi: 10.1061/(ASCE)HE.1943-5584.0000040 – volume: 375 start-page: 613 issue: 3 year: 2009 ident: 10.1016/j.scitotenv.2017.09.262_bb0110 article-title: Ensemble flood forecasting: a review publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2009.06.005 – volume: 157 start-page: 310 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0080 article-title: Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling publication-title: Catena doi: 10.1016/j.catena.2017.05.034 – year: 2007 ident: 10.1016/j.scitotenv.2017.09.262_bb0440 – volume: 75 start-page: 665 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0510 article-title: GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran publication-title: Environ. Earth Sci. doi: 10.1007/s12665-016-5424-9 – volume: 59 start-page: 1261 issue: 3 year: 2011 ident: 10.1016/j.scitotenv.2017.09.262_bb0075 article-title: Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan publication-title: Nat. Hazards doi: 10.1007/s11069-011-9831-7 – volume: 38 start-page: 1083 issue: 10 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb9000 article-title: Comparison of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2016.07.003 – volume: 6 start-page: 58 issue: 1 year: 2002 ident: 10.1016/j.scitotenv.2017.09.262_bb0105 article-title: The particle swarm-explosion, stability and convergence in a multidimensional complex space publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.985692 – volume: 5 start-page: 3 issue: 1 year: 1991 ident: 10.1016/j.scitotenv.2017.09.262_bb0260 article-title: Digital terrain modelling: a review of hydrological, geomorphological, and biological applications publication-title: Hydrol. Process. doi: 10.1002/hyp.3360050103 – ident: 10.1016/j.scitotenv.2017.09.262_bb0155 – volume: 3 start-page: 85 issue: 1 year: 2015 ident: 10.1016/j.scitotenv.2017.09.262_bb0215 article-title: Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam, Iran publication-title: Environ. Resour. Res. – volume: 344 start-page: 243 year: 2005 ident: 10.1016/j.scitotenv.2017.09.262_bb0135 article-title: Ant colony optimization theory: a survey publication-title: Theor. Comput. Sci. doi: 10.1016/j.tcs.2005.05.020 – volume: 188 start-page: 44 issue: 1 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0270 article-title: GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran publication-title: Environ. Monit. Assess. doi: 10.1007/s10661-015-5049-6 – volume: 72 start-page: 4001 issue: 10 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0455 article-title: Flood susceptibility mapping using integrated bivariate and multivariate statistical models publication-title: Environ. Earth Sci. doi: 10.1007/s12665-014-3289-3 – volume: 394 year: 2002 ident: 10.1016/j.scitotenv.2017.09.262_bb0280 – volume: 411 start-page: 290 issue: 3 year: 2011 ident: 10.1016/j.scitotenv.2017.09.262_bb0305 article-title: GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2011.10.010 – volume: 38 start-page: 8208 issue: 7 year: 2011 ident: 10.1016/j.scitotenv.2017.09.262_bb0425 article-title: Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang Valley Malaysia publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.12.167 – start-page: 895 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0230 article-title: Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS – volume: 29 start-page: 1149 issue: 4 year: 2015 ident: 10.1016/j.scitotenv.2017.09.262_bb0460 article-title: Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-015-1021-9 – volume: 75 start-page: 1 issue: 11 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0395 article-title: A novel hybrid KPCA and SVM with PSO model for identifying debris flow hazard degree: a case study in Southwest China publication-title: Environ. Earth Sci. doi: 10.1007/s12665-016-5774-3 – volume: 71 start-page: 523 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0290 article-title: Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models publication-title: Nat. Hazards doi: 10.1007/s11069-013-0932-3 – volume: 538 start-page: 555 year: 2015 ident: 10.1016/j.scitotenv.2017.09.262_bb0170 article-title: Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: application in Rhodope–Evros region, Greece publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2015.08.055 – volume: 188 start-page: 656 issue: 12 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0185 article-title: Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon's entropy, statistical index, and weighting factor models publication-title: Environ. Model. Assess. doi: 10.1007/s10661-016-5665-9 – volume: 4 start-page: 1942 year: 1995 ident: 10.1016/j.scitotenv.2017.09.262_bb0175 – volume: 85 start-page: 239 issue: 1 year: 2001 ident: 10.1016/j.scitotenv.2017.09.262_bb0330 article-title: Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA publication-title: Agric. Ecosyst. Environ. doi: 10.1016/S0167-8809(01)00187-6 – volume: 6 start-page: 67 issue: 1 year: 2006 ident: 10.1016/j.scitotenv.2017.09.262_bb0430 article-title: A comparison between genetic algorithms and sequential quadratic programming in solving constrained optimization problems publication-title: ICGST Int. J. Artif. Intell. Mach. Lear. – volume: 12 start-page: 921 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0285 article-title: The impact of flood damages on production of Iran's Agricultural Sector. Middle East publication-title: J. Sci. Res. – start-page: 1991 year: 1996 ident: 10.1016/j.scitotenv.2017.09.262_bb0205 article-title: LVQ PAK: the learning vector quantization program package – volume: 16 start-page: 71 issue: 6 year: 2006 ident: 10.1016/j.scitotenv.2017.09.262_bb0320 article-title: A hybrid medical decision making system based on principle component analysis, k-NN based weighted pre-processing and adaptive neuro fuzzy inference system publication-title: Digital Signal Process. doi: 10.1016/j.dsp.2006.05.001 – volume: 225 start-page: 486 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0350 article-title: Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon's entropy and GIS publication-title: Appl. Mech. Mater. doi: 10.4028/www.scientific.net/AMM.225.486 – volume: 290 start-page: 569 year: 1990 ident: 10.1016/j.scitotenv.2017.09.262_bb0255 article-title: Morphometric assessment of lithologic controls on drainage basin evolution in the Crawford upland, south-central Indiana publication-title: Am. J. Sci. doi: 10.2475/ajs.290.5.569 – volume: 31 start-page: 42 issue: 1 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0405 article-title: Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golestan Province, Iran publication-title: Geocarto Int. doi: 10.1080/10106049.2015.1041559 – start-page: 432 year: 2005 ident: 10.1016/j.scitotenv.2017.09.262_bb0490 – volume: 63 start-page: 965 issue: 2 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0355 article-title: Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran publication-title: Nat. Hazards doi: 10.1007/s11069-012-0217-2 – volume: 2301 start-page: 35 year: 2013 ident: 10.1016/j.scitotenv.2017.09.262_bb0100 – volume: 67 start-page: 251 issue: 1 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0195 article-title: An artificial neural network model for flood simulation using GIS: Johor River Basin Malaysia publication-title: Environ. Earth Sci. doi: 10.1007/s12665-011-1504-z – volume: 253 start-page: 508 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0150 article-title: Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model publication-title: Geomorphology doi: 10.1016/j.geomorph.2015.10.030 – volume: 38 start-page: 10568 issue: 8 year: 2011 ident: 10.1016/j.scitotenv.2017.09.262_bb0500 article-title: A hybrid GA–TS algorithm for open vehicle routing optimization of coal mines material publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.02.108 – volume: 69 start-page: 749 issue: 1 year: 2013 ident: 10.1016/j.scitotenv.2017.09.262_bb0360 article-title: Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances publication-title: Nat. Hazards doi: 10.1007/s11069-013-0728-5 – volume: 75 start-page: 1 issue: 3 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0340 article-title: Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran publication-title: Environ. Earth Sci. doi: 10.1007/s12665-015-4950-1 – volume: 17 year: 2004 ident: 10.1016/j.scitotenv.2017.09.262_bb0010 – volume: 15 start-page: 617 issue: 2 year: 2011 ident: 10.1016/j.scitotenv.2017.09.262_bb0165 article-title: Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-15-617-2011 – volume: 504 start-page: 69 year: 2013 ident: 10.1016/j.scitotenv.2017.09.262_bb0450 article-title: Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.09.034 – volume: 378 start-page: 325 issue: 3 year: 2009 ident: 10.1016/j.scitotenv.2017.09.262_bb0415 article-title: Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2009.09.037 – volume: 382 start-page: 20 issue: 1 year: 2010 ident: 10.1016/j.scitotenv.2017.09.262_bb0475 article-title: Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2009.12.013 – volume: 297 start-page: 69 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0090 article-title: Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques publication-title: Geomorphology doi: 10.1016/j.geomorph.2017.09.007 – volume: 109 start-page: 108 issue: 3 year: 2009 ident: 10.1016/j.scitotenv.2017.09.262_bb0420 article-title: Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains Japan publication-title: Geomorphology doi: 10.1016/j.geomorph.2009.02.026 – volume: 22 start-page: 643 issue: 3 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0375 article-title: GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran publication-title: Hydrolith. J. – volume: 34 start-page: 3099 issue: 4 year: 2008 ident: 10.1016/j.scitotenv.2017.09.262_bb0480 article-title: An adaptive neuro-fuzzy inference system for bridge risk assessment publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2007.06.026 – volume: 30 start-page: 662 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0335 article-title: Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran publication-title: Geocarto Int. doi: 10.1080/10106049.2014.966161 – volume: 11 start-page: 909 issue: 4 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0160 article-title: GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran publication-title: Int. J. Environ. Sci. Technol. doi: 10.1007/s13762-013-0464-0 – volume: 470 start-page: 302 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0125 article-title: Classifiers for the detection of flood-prone areas using remote sensed elevation data publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2012.09.006 – volume: 51 start-page: 350 year: 2013 ident: 10.1016/j.scitotenv.2017.09.262_bb0385 article-title: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2012.08.023 – volume: 25 start-page: 1223-1217 issue: 7 year: 2005 ident: 10.1016/j.scitotenv.2017.09.262_bb0410 article-title: Heat exchanger network synthesis and optimization using genetic algorithm publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2004.06.024 – volume: 24 start-page: 43 year: 1979 ident: 10.1016/j.scitotenv.2017.09.262_bb0040 article-title: A physically based, variable contributing area model of basin hydrology publication-title: Hydrol. Sci. Bull. doi: 10.1080/02626667909491834 – volume: 3 start-page: 71 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0445 article-title: Morphometric analysis of a Semi Urban Watershed, trans Yamuna, draining at Allahabad using Cartosat (DEM) data and GIS publication-title: Int. J. Eng. Sci. – start-page: 4796 year: 2010 ident: 10.1016/j.scitotenv.2017.09.262_bb0315 article-title: A fuzzy-logic-based approach for flood detection from Cosmo-SkyMed data – volume: 4 start-page: 169 issue: 3 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0210 article-title: Performance assessment of two “LNRF” and “AHP-Area Density” models in landslide susceptibility zonation publication-title: J. Life Sci. Biomed. – volume: 10 start-page: 485 issue: 6 year: 2005 ident: 10.1016/j.scitotenv.2017.09.262_bb0070 article-title: Comparison of several flood forecasting models in Yangtze River publication-title: J. Hydraul. Eng. – volume: 1 start-page: 85 issue: 2 year: 2012 ident: 10.1016/j.scitotenv.2017.09.262_bb0435 article-title: A methodology based on spatial distribution of parameters for understanding affect of rainfall and vegetation density on groundwater recharge publication-title: Eur. J. Sustain. Dev. doi: 10.14207/ejsd.2012.v1n2p85 – volume: 513 start-page: 283 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0275 article-title: Application of GIS based data driven evidential belief function model to predict groundwater potential zonation publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.02.053 – volume: 47 start-page: 65 issue: 1 year: 2008 ident: 10.1016/j.scitotenv.2017.09.262_bb0145 article-title: Flood hazard in Hunan province of China: an economic loss analysis publication-title: Nat. Hazards doi: 10.1007/s11069-007-9197-z – volume: 514 start-page: 418 year: 2015 ident: 10.1016/j.scitotenv.2017.09.262_bb0095 article-title: Assessing urban potential flooding risk and identifying effective risk-reduction measures publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2015.02.027 – volume: 83 start-page: 947 issue: 2 year: 2016 ident: 10.1016/j.scitotenv.2017.09.262_bb0190 article-title: A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique publication-title: Nat. Hazards doi: 10.1007/s11069-016-2357-2 – volume: 158 start-page: 194 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0180 article-title: Modeling soil cation exchange capacity in multiple countries publication-title: Catena doi: 10.1016/j.catena.2017.07.002 – volume: 7 start-page: 1857 issue: 5 year: 2014 ident: 10.1016/j.scitotenv.2017.09.262_bb0365 article-title: GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran) publication-title: Arab. J. Geosci. doi: 10.1007/s12517-012-0825-x – volume: 609 start-page: 764 year: 2017 ident: 10.1016/j.scitotenv.2017.09.262_bb0370 article-title: Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2017.07.198 |
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| Snippet | Flood is one of the most destructive natural disasters which cause great financial and life losses per year. Therefore, producing susceptibility maps for flood... |
| SourceID | wageningen proquest pubmed crossref elsevier |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 438 |
| SubjectTerms | algorithms altitude ANFIS Ant colony computer software disasters flood control Flood susceptibility mapping fuzzy logic Genetic algorithm geographic information systems land cover land use planning model validation neural networks Particle swarm optimization rain rivers streams system optimization |
| Title | Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms |
| URI | https://dx.doi.org/10.1016/j.scitotenv.2017.09.262 https://www.ncbi.nlm.nih.gov/pubmed/28988080 https://www.proquest.com/docview/1949083548 https://www.proquest.com/docview/2000531350 http://www.narcis.nl/publication/RecordID/oai:library.wur.nl:wurpubs%2F528541 |
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