Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles

Bauer, 1999, An empirical comparison of voting classification algorithms: bagging, boosting, and variants, Mach. Learn., 36, 105, 10.1023/A:1007515423169 Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655 Chapi, 2017, A novel hybrid artificial intelligence approach for flood susceptibility assessment, Environ. Modell. Software, 95, 229, 10.1016/j.envsoft.2017.06.012 Chen, 2018, GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models, Sci. Total Environ., 634, 853, 10.1016/j.scitotenv.2018.04.055 Chen, 2019, Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization, J. Hydrol., 572, 435, 10.1016/j.jhydrol.2019.03.013 Chen, 2019, Novel hybrid integration approach of bagging-based Fisher’s linear discriminant function for groundwater potential analysis, Nat. Resour. Res., 1 Chen, 2018, Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling, Bull. Eng. Geol. Environ., 1 Chen, 2018, Landslide susceptibility modeling based on gis and novel bagging-based kernel logistic regression, Appl. Sci., 8, 2540, 10.3390/app8122540 Chen, 2017, A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China, Geomatics, Natural Hazards Risk, 8, 1955, 10.1080/19475705.2017.1401560 Chen, 2019, Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models, Appl. Sci., 9, 171, 10.3390/app9010171 Chen, 2019, Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods, Science Total Environ., 684, 31, 10.1016/j.scitotenv.2019.05.312 Chen, 2019, Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree, Geocarto Int., 1 Degiorgis, 2012, Classifiers for the detection of flood-prone areas using remote sensed elevation data, J. Hydrol., 470–471, 302, 10.1016/j.jhydrol.2012.09.006 García-Pintado, 2013, Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling, J. Hydrol., 495, 252, 10.1016/j.jhydrol.2013.03.050 Gnecco, 2017, Supervised and semi-supervised classifiers for the detection of flood-prone areas, Soft. Comput., 21, 3673, 10.1007/s00500-015-1983-z Kuncheva, 2010, Random subspace ensembles for FMRI classification, IEEE Trans. Med. Imag., 29, 531, 10.1109/TMI.2009.2037756 Hong, 2018, Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution, Sci. Total Environ., 621, 1124, 10.1016/j.scitotenv.2017.10.114 Hong, 2018, Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China, Sci. Total Environ., 625, 575, 10.1016/j.scitotenv.2017.12.256 Huang, 2008, Flood hazard in Hunan province of China: an economic loss analysis, Nat. Hazards, 47, 65, 10.1007/s11069-007-9197-z Hyndman, 2016 Jayanthi, 2013, Reptree classifier for identifying link spam in web search engines, ICTACT J. Soft. Comput., 3 Joseph, 2012, A comparative evaluation of software effort estimation using REPTree and K* in handling with missing values, Aust. J. Basic Appl. Sci., 6, 312 Khosravi, 2016, 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, Nat. Hazards, 83, 947, 10.1007/s11069-016-2357-2 Khosravi, 2018, A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran, Sci. Total Environ., 627, 744, 10.1016/j.scitotenv.2018.01.266 Khosravi, 2016, 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, Environ. Monit. Assess., 188, 656, 10.1007/s10661-016-5665-9 Kia, 2012, An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia, Environ. Earth Sci., 67, 251, 10.1007/s12665-011-1504-z Ko, 2018, From landslide susceptibility to landslide frequency: a territory-wide study in Hong Kong, Eng. Geol., 242, 12, 10.1016/j.enggeo.2018.05.001 Kourgialas, 2011, Flood management and a GIS modelling method to assess flood-hazard areasâ”a case study, Int. Assoc. Sci. Hydrol. Bull., 56, 212, 10.1080/02626667.2011.555836 Kuncheva, L.I., Rodriguez, J.J., Plumpton, C.O., Linden, D.E., Johnston, S.J., 2010. Random subspace ensembles for FMRI classification. IEEE Transactions on Medical Imaging, 29, 531-542.Lakshmi, D.C., 2015. Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast. Eprint Arxiv. Lee, 2012, Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS, 895 Levy, 2007, Multi-criteria decision support systems for flood hazard mitigation and emergency response in urban watersheds, JAWRA J. Am. Water Resour. Assoc., 43, 346, 10.1111/j.1752-1688.2007.00027.x Maier, 2010, Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions, Environ. Modell. Software, 25, 891, 10.1016/j.envsoft.2010.02.003 Manfreda, 2015, Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models, Nat. Hazards, 79, 735, 10.1007/s11069-015-1869-5 Norbiato, 2008, Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins, J. Hydrol., 362, 274, 10.1016/j.jhydrol.2008.08.023 Norbiato, D., Borga, M., Degli Esposti, S., Gaume, E., Anquetin, S., 2008. Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins. Journal of Hydrology, 362, 274-290.Oh, 2011, Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area, Comput. Geosci., 37, 1264, 10.1016/j.cageo.2010.10.012 Pradhan, 2010, Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing, J. Spatial Hydrol., 9 Prasad, 2006, Newer classification and regression tree techniques: bagging and random forests for ecological prediction, Ecosystems, 9, 181, 10.1007/s10021-005-0054-1 Rahmati, 2016, Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran, Geocarto International, 31, 42, 10.1080/10106049.2015.1041559 Razavi Termeh, 2018, Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms, Sci. Total Environ., 615, 438, 10.1016/j.scitotenv.2017.09.262 Samela, 2017, Geomorphic classifiers for flood-prone areas delineation for data-scarce environments, Adv. Water Resour., 102, 13, 10.1016/j.advwatres.2017.01.007 Sarhadi, 2012, Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis, J. Hydrol., 458-459, 68, 10.1016/j.jhydrol.2012.06.039 Sarhadi, A., Soltani, S., Modarres, R., 2012. Probabilistic flood inundation mapping of ungauged rivers: Linking GIS techniques and frequency analysis. Journal of Hydrology, 458-459, 68-86.Shafapour Tehrany, 2017, GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques, Geomatics, Natural Hazards Risk, 8, 1538, 10.1080/19475705.2017.1362038 Shafizadeh-Moghadam, 2018, Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping, J. Environ. Manage., 217, 1, 10.1016/j.jenvman.2018.03.089 Skurichina, 2002, Bagging, boosting and the random subspace method for linear classifiers, Pattern Anal. Appl., 5, 121, 10.1007/s100440200011 Su, 2015, Mapping of rainfall-induced landslide susceptibility in Wencheng, China, using support vector machine, Nat. Hazards, 76, 1759, 10.1007/s11069-014-1562-0 Tao, 2006, Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval, IEEE Trans. Pattern Anal. Mach. Intell., 28, 1088, 10.1109/TPAMI.2006.134 Tehrany, 2013, Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS, J. Hydrol., 504, 69, 10.1016/j.jhydrol.2013.09.034 Tehrany, 2014, Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS, J. Hydrol., 512, 332, 10.1016/j.jhydrol.2014.03.008 Tehrany, 2015, Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method, Stoch. Env. Res. Risk Assess., 29, 1149, 10.1007/s00477-015-1021-9 Termeh, 2018, Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms, Sci. Total Environ., 615, 438, 10.1016/j.scitotenv.2017.09.262 Tien Bui, 2016, Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS, J. Hydrol., 540, 317, 10.1016/j.jhydrol.2016.06.027 Witten, 2011 Youssef, 2011, Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery, Environ. Earth Sci., 62, 611, 10.1007/s12665-010-0551-1 Zhang, 2015, Examining the influence of river–lake interaction on the drought and water resources in the Poyang Lake basin, J. Hydrol., 522, 510, 10.1016/j.jhydrol.2015.01.008 Zhao, 2018, Mapping flood susceptibility in mountainous areas on a national scale in China, Sci. Total Environ., 615, 1133, 10.1016/j.scitotenv.2017.10.037