{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T17:01:58Z","timestamp":1782234118157,"version":"3.54.5"},"reference-count":135,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["DK W 1237-N23"],"award-info":[{"award-number":["DK W 1237-N23"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular due to open data access and lesser prediction times. There is a continuous effort to improve the prediction accuracy of these models through introducing new methods. This study is focused on flash flood modeling through novel hybrid machine learning models, which can improve the prediction accuracy. The hybrid machine learning ensemble approaches that combine the three meta-classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) with J48 (a tree-based algorithm that can be used to evaluate the behavior of the attribute vector for any defined number of instances) were used in the Gorganroud River Basin of Iran to assess flood susceptibility (FS). A total of 426 flood positions as dependent variables and a total of 14 flood conditioning factors (FCFs) as independent variables were used to model the FS. Several threshold-dependent and independent statistical tests were applied to verify the performance and predictive capability of these machine learning models, such as the receiver operating characteristic (ROC) curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E), root-mean square-error (RMSE), and true skill statistics (TSS). The valuation of the FCFs was done using AdaBoost, frequency ratio (FR), and Boosted Regression Tree (BRT) models. In the flooding of the study area, altitude, land use\/land cover (LU\/LC), distance to stream, normalized differential vegetation index (NDVI), and rainfall played important roles. The Random Subspace J48 (RSJ48) ensemble method with an area under the curve (AUC) of 0.931 (SRC), 0.951 (PRC), E of 0.89, sensitivity of 0.87, and TSS of 0.78, has become the most effective ensemble in predicting the FS. The FR technique also showed good performance and reliability for all models. Map removal sensitivity analysis (MRSA) revealed that the FS maps have the highest sensitivity to elevation. Based on the findings of the validation methods, the FS maps prepared using the machine learning ensemble techniques have high robustness and can be used to advise flood management initiatives in flood-prone areas.<\/jats:p>","DOI":"10.3390\/rs12203423","type":"journal-article","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T21:26:06Z","timestamp":1603056366000},"page":"3423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-1666","authenticated-orcid":false,"given":"Alireza","family":"Arabameri","sequence":"first","affiliation":[{"name":"Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2739-3716","authenticated-orcid":false,"given":"Sunil","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, Malda 732103, West Bengal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaustuv","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Department of Geography, Chandidas Mahavidyalaya, Birbhum 731215, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2013Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources, Xi\u2019an 710021, China"},{"name":"Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6574-5762","authenticated-orcid":false,"given":"Phuong Thao Thi","family":"Ngo","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi 10000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6605-498X","authenticated-orcid":false,"given":"Shahab S.","family":"Band","sequence":"additional","affiliation":[{"name":"Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan"},{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.sbspro.2011.05.169","article-title":"Case Study on Seasonal Floods in Iran, Watershed of Ghotour Chai Basin","volume":"19","author":"Asgharpour","year":"2011","journal-title":"Procedia Soc. Behav. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/esp.1360","article-title":"Impact of natural reforestation on floodplain sedimentation in the Dragonja basin, SW Slovenia","volume":"32","author":"Keesstra","year":"2007","journal-title":"Earth Surf. Process. Landf. J. Br. Geomorphol. Res. Group"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.apgeog.2008.02.007","article-title":"Geographical analysis of damage due to flash floods in southern France: The cases of 12\u201313 November 1999 and 8\u20139 September 2002","volume":"28","author":"Vinet","year":"2008","journal-title":"Appl. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gharaibeh, A.A., Zu\u2019bi, A., Esra\u2019a, M., and Abuhassan, L.B. (2019). Amman (City of Waters); Policy, Land Use, and Character Changes. Land, 8.","DOI":"10.3390\/land8120195"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s11069-019-03749-3","article-title":"The use of watershed geomorphic data in flash flood susceptibility zoning: A case study of the Karnaphuli and Sangu river basins of Bangladesh","volume":"99","author":"Adnan","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/nclimate1911","article-title":"Global flood risk under climate change","volume":"3","author":"Hirabayashi","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_7","unstructured":"CRED, and UNISDR (2015). The Human Cost of Weather-Related Disasters, 1995\u20132015."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Casale, R., and Margottini, C. (1999). Floods and Landslides, Integrated Risk Assessment, Integrated Risk Assessment, Springer Science & Business Media. with 30 Tables.","DOI":"10.1007\/978-3-642-58609-5"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Smith, K. (2013). Environmental Hazards, Assessing Risk and Reducing Disaster, Routledge.","DOI":"10.4324\/9780203805305"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s41976-019-00018-6","article-title":"Application of the GIS-Based Probabilistic Models for Mapping the Flood Susceptibility in Bansloi Sub-basin of Ganga-Bhagirathi River and Their Comparison","volume":"15","author":"Paul","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"138747","DOI":"10.1016\/j.scitotenv.2020.138747","article-title":"The potential of Tidal River Management for flood alleviation in South Western Bangladesh","volume":"731","author":"Adnan","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111","DOI":"10.5194\/soil-2-111-2016","article-title":"The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals","volume":"2","author":"Keesstra","year":"2016","journal-title":"Soil"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Keesstra, S., Mol, G., de Leeuw, J., Okx, J., de Cleen, M., and Visser, S. (2018). Soil-related sustainable development goals: Four concepts to make land degradation neutrality and restoration work. Land, 7.","DOI":"10.3390\/land7040133"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Visser, S., Keesstra, S., Maas, G., and De Cleen, M. (2019). Soil as a Basis to Create Enabling Conditions for Transitions Towards Sustainable Land Management as a Key to Achieve the SDGs by 2030. Sustainability, 11.","DOI":"10.3390\/su11236792"},{"key":"ref_15","unstructured":"(2019, September 20). Algeria: State Owned Reinsurer Shows Strong Technical Results, Good Investment Returns. Available online: https:\/\/www.meinsurancereview.com\/News\/View-NewsLetterArticle?id=46352&Type=MiddleEast."},{"key":"ref_16","first-page":"921","article-title":"The impact of flood damages on production of Iran\u2019s agricultural sector","volume":"12","author":"Norouzi","year":"2012","journal-title":"Middle East J. Sci. Res."},{"key":"ref_17","unstructured":"Jannati, H. (2019, September 20). History of the Devastating Floods in Iran. Political Studies and Research Institute 593 of Iran, pr 12. Available online: http\/\/ir-psri.com\/?Page=ViewNews&NewsID=6283."},{"key":"ref_18","first-page":"1817","article-title":"Flood Risk Assessment Using GIS (Case Study, Golestan Province, Iran)","volume":"21","author":"Safaripour","year":"2012","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Ozturk, P., and Chau, K.W. (2018). Flood Prediction Using Machine Learning Models: Literature Review. Water, 10.","DOI":"10.20944\/preprints201810.0098.v2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.envsoft.2015.09.009","article-title":"Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level","volume":"75","author":"Kauffeldt","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.aqpro.2015.02.126","article-title":"A Review on Hydrological Models","volume":"4","author":"Devia","year":"2015","journal-title":"Aquat. Procedia"},{"key":"ref_22","first-page":"352","article-title":"A Critical Review of Hydrological Modeling Practices for Flood Management","volume":"9","author":"Chourushi","year":"2019","journal-title":"Pramana Res. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"06028","DOI":"10.1051\/e3sconf\/20184006028","article-title":"Flood forecasting using a coupled hydrological and hydraulic model (based on FVM) and highresolution meteorological model","volume":"40","author":"Amengual","year":"2018","journal-title":"E3S Web Conf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1080\/02626667.2018.1474219","article-title":"Evaluation of four hydrological models for operational flood forecasting in a Canadian Prairie watershed","volume":"63","author":"Unduche","year":"2018","journal-title":"Hydrol. Sci. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.envsoft.2015.01.009","article-title":"Enhancing river model set-up for 2-D dynamic flood modelling","volume":"67","author":"Costabile","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.22499\/2.6001.002","article-title":"Comparison of two seasonal rainfall forecasting systems for Australia","volume":"60","author":"Fawcett","year":"2010","journal-title":"Aust. Meteorol. Oceanogr. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Karimi-Sangchini, E., Chandra Pal, S., Saha, A., Chowdhuri, I., Lee, S., and Tien Bui, D. (2020). Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility. Remote Sens., 12.","DOI":"10.3390\/rs12203389"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"133","DOI":"10.2495\/FRIAR120111","article-title":"Comparison of a data-driven model and a physical model for flood forecasting","volume":"159","author":"Ji","year":"2012","journal-title":"WIT Trans. Ecol. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jhydrol.2014.02.053","article-title":"Application of GIS based data driven evidential belief function model to predict groundwater potential zonation","volume":"513","author":"Nampak","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jhydrol.2013.09.034","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","volume":"11","author":"Tehrany","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s12665-015-4830-8","article-title":"Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models","volume":"75","author":"Youssef","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide susceptibility mapping using J48 decision tree with AdaBoost, bagging and rotation forest ensembles in the Guangchang area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.scitotenv.2017.12.256","article-title":"Application of fuzzy weight of evidence and datamining techniques in construction of flood susceptibility map of Poyang County, China","volume":"625","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jhydrol.2016.06.027","article-title":"Hybrid artificial intelligence approach based on neural fuzzy inference model and meta heuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS","volume":"540","author":"Pradhan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00477-015-1021-9","article-title":"Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method","volume":"29","author":"Tehrany","year":"2015","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s12665-018-7667-0","article-title":"The application of a Dempster\u2013Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods","volume":"77","author":"Tehrany","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.scitotenv.2019.01.021","article-title":"A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran","volume":"660","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"05014006","DOI":"10.1061\/(ASCE)CP.1943-5487.0000360","article-title":"Developing strategies for urban flood management of Tehran city using SMCDM and ANN","volume":"28","author":"Radmehr","year":"2014","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s11069-011-9831-7","article-title":"Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan","volume":"59","author":"Chen","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s11069-013-0639-5","article-title":"Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP)","volume":"68","author":"Stefanidis","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s00477-012-0598-5","article-title":"Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP","volume":"27","author":"Zou","year":"2013","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.scitotenv.2015.08.055","article-title":"Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process, Application in Rhodope\u2013Evros region, Greece","volume":"538","author":"Kazakis","year":"2015","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/10106049.2015.1041559","article-title":"Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran","volume":"31","author":"Rahmati","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jenvman.2011.09.016","article-title":"Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping","volume":"96","author":"Lee","year":"2012","journal-title":"J. Environ. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12403-014-0135-5","article-title":"Assessment of the contribution of N-fertilizers to nitrate pollution of groundwater in western Iran (case study, Ghorveh\u2013DehgelanArquifer)","volume":"7","author":"Rahmati","year":"2015","journal-title":"Water Qual. Expo. Health"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Blaschke, T., Tiefenbacher, J.P., Pradhan, B., and Bui, D.T. (2020). Gully Head-Cut Distribution Modeling Using Machine Learning Methods\u2014A Case Study of N.W. Iran. Water, 12.","DOI":"10.3390\/w12010016"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Cerda, A., Pradhan, B., Tiefenbacher, J.P., Lombardo, L., and Bui, D.T. (2020). A methodological comparison of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence. Geomorphology, 107136.","DOI":"10.1016\/j.geomorph.2020.107136"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Lee, S., Tiefenbacher, J.P., and Ngo, P.T.T. (2020). Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran). Remote Sens., 12.","DOI":"10.3390\/rs12030490"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Blaschke, T., Pradhan, B., Pourghasemi, H.R., Tiefenbacher, J.P., and Bui, D.T. (2020). Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study. Sensors, 20.","DOI":"10.3390\/s20020335"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11069-016-2304-2","article-title":"Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS","volume":"83","author":"Pham","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2012.03.003","article-title":"Application of an evidential belief function model in landslide susceptibility mapping","volume":"44","author":"Althuwaynee","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1080\/19475705.2017.1401560","article-title":"A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naive Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China","volume":"8","author":"Chen","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10346-016-0711-9","article-title":"Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization","volume":"14","author":"Tuan","year":"2017","journal-title":"Landslides"},{"key":"ref_54","first-page":"211","article-title":"Groundwater potential mapping using a novel data-mining ensemble model","volume":"27","author":"Kordestani","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jhydrol.2018.08.027","article-title":"Groundwater spring potential modelling, Comprising the capability and robustness of three different modeling approaches","volume":"565","author":"Rahmati","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"135161","DOI":"10.1016\/j.scitotenv.2019.135161","article-title":"Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method","volume":"711","author":"Hosseini","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Janizadeh, S., Avand, M., and Jaafari, A. (2019). Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability, 11.","DOI":"10.3390\/su11195426"},{"key":"ref_58","unstructured":"Edwards, P.K., Duhon, D., and Shergill, S. (2019). Real AdaBoost, Boosting for Credit Scorecards and Similarity to WOE Logistic Regression, Scotiabank."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TPAMI.2006.134","article-title":"Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval","volume":"28","author":"Tao","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.patrec.2007.10.005","article-title":"Random subspace for an improved biohashing for face authentication","volume":"29","author":"Nanni","year":"2008","journal-title":"Pattern Recogn. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1016\/j.patcog.2006.12.002","article-title":"A linear discriminant analysis framework based on random subspace for face recognition","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.imavis.2008.12.009","article-title":"Semi-random subspace method for face recognition","volume":"27","author":"Zhu","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1007\/s12665-017-6981-2","article-title":"A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)","volume":"76","author":"Hong","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1023\/A:1007659514849","article-title":"MultiBoosting, a technique for combining boosting and wagging","volume":"40","author":"Webb","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_65","unstructured":"IRIMO (2019, September 20). Summary Reports of Iran\u2019s Extreme Climatic Events. Ministry of Roads and Urban Development, Iran Meteorological Organization, Available online: www.cri.ac.ir."},{"key":"ref_66","unstructured":"GSI (2019, September 20). Geology Survey of Iran. Available online: http\/\/www.gsi.ir\/Main\/Lang_en\/index.html."},{"key":"ref_67","unstructured":"(2019, September 20). Donya-e-Eqtesad. Available online: https\/\/www.donya-e-eqtesad.com\/fa\/tiny\/news-5863511460."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"100207","DOI":"10.1016\/j.crm.2019.100207","article-title":"Inundation modelling for Bangladeshi coasts usingdownscaled and bias-corrected temperature","volume":"27","author":"Hasan","year":"2020","journal-title":"Clim. Risk Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"281","DOI":"10.5194\/isprsarchives-XXXIX-B4-281-2012","article-title":"Validation of the ASTER global digital elevation model version 2 over the conterminous United States","volume":"B4","author":"Gesch","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"125007","DOI":"10.1016\/j.jhydrol.2020.125007","article-title":"Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques","volume":"587","author":"Arabameri","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"104545","DOI":"10.1016\/j.catena.2020.104545","article-title":"Spatial modelling of gully erosion in the Ardib River Watershed using three statistical-based techniques","volume":"190","author":"Arabameri","year":"2020","journal-title":"Catena"},{"key":"ref_72","first-page":"67","article-title":"Prediction of gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: A case study in the Mazayejan Plain, Southwest Iran","volume":"37","author":"Zakerinejad","year":"2014","journal-title":"Suppl. Geogr. Fis. Din. Quat."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s12665-016-5919-4","article-title":"GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks","volume":"75","author":"Bui","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1007\/s10661-016-5665-9","article-title":"Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: A comparison between Shannon\u2019s entropy, statistical index, andweighting factor models","volume":"188","author":"Khosravi","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s11069-015-1605-1","article-title":"Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya","volume":"77","author":"Meraj","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s11069-016-2357-2","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","volume":"83","author":"Khosravi","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_77","first-page":"229","article-title":"Individual preferences for reducing flood risk to near zero through elevation","volume":"2","author":"Botzen","year":"2012","journal-title":"Mitig. Adapt. Strateg. Glob. Chang."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology","volume":"24","author":"Kirkby","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.enggeo.2005.07.011","article-title":"The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity","volume":"81","author":"Gokceoglu","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_80","first-page":"23","article-title":"A terrain ruggedness index that quantifies topographic heterogeneity","volume":"5","author":"Riley","year":"1999","journal-title":"Intermt. J. Sci."},{"key":"ref_81","unstructured":"Wilson, J.P., and Gallant, J.C. (2000). Primary topographic attributes. Terrain Analysis, Principles and Applications, Wiley."},{"key":"ref_82","unstructured":"Weiss, A. (2001, January 9). Topographic position and landforms analysis. Proceedings of the Poster Presentation, ESRI User Conference, San Diego, CA, USA."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1016\/j.cageo.2008.12.014","article-title":"Comparison of roving-window and search-window techniques for characterising landscape morphometry","volume":"35","author":"Grohmann","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modelling: A review of hydrological, geomorphological, and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_85","first-page":"16","article-title":"Determination of drainage network in digital elevation model, utilities and limitations","volume":"2","author":"Kiss","year":"2004","journal-title":"J. Hung. Geo-Math."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","article-title":"A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran","volume":"627","author":"Khosravi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1126\/science.1178256","article-title":"Sustainable floodplains through large-scale reconnection to rivers","volume":"3265959","author":"Opperman","year":"2009","journal-title":"Science"},{"key":"ref_88","first-page":"146","article-title":"Kriging and thin plate splines for mapping climate variables","volume":"3","author":"Boer","year":"2001","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1002\/ldr.3058","article-title":"Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process, Application of the fuzzy decision-making trial and evaluation laboratory approach","volume":"29","author":"Choubin","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_90","unstructured":"Lo, C.P., and Yeung, A.K.W. (2002). Concepts and Techniques of Geographic Information System, Pearson Education Inc."},{"key":"ref_91","first-page":"1","article-title":"Flood susceptible mapping and risk area estimation using logistic regression, GIS and remote sensing","volume":"9","author":"Pradhan","year":"2010","journal-title":"J. Spat. Hydrol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Nalivan, O.A., Saha, S., Roy, J., Pradhan, B., Tiefenbacher, J.P., and Ngo, P.T.T. (2020). Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility. Remote Sens., 12.","DOI":"10.3390\/rs12111890"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Lombardo, L., Blaschke, T., and Tien Bui, D. (2020). Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12010140"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1016\/j.gsf.2019.11.009","article-title":"Comparison of machine learning models for gully erosion susceptibility mapping","volume":"11","author":"Arabameri","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Roy, J., Saha, S., Arabameri, A., Blaschke, T., and Bui, D.T. (2019). A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India. Remote Sens., 11.","DOI":"10.3390\/rs11232866"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Cerda, A., Rodrigo-Comino, J., Pradhan, B., Sohrabi, M., Blaschke, T., and Bui, D.T. (2019). Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran). Remote Sens., 11.","DOI":"10.3390\/rs11212577"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.scitotenv.2019.02.436","article-title":"Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms","volume":"668","author":"Gayen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2006","journal-title":"Landslides"},{"key":"ref_99","first-page":"13","article-title":"Improved J48 classification algorithm for the prediction of diabetes","volume":"98","author":"Kaur","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_100","unstructured":"Witten, H.I., Frank, E., and Mark, A. (2011). Hall Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression, a statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Ho, T.K. (1998). Nearest neighbors in random subspaces. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Springer.","DOI":"10.1007\/BFb0033288"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s10462-010-9192-8","article-title":"Combining bagging, boosting, rotation forest and random subspace methods","volume":"35","author":"Kotsiantis","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1109\/TMI.2009.2037756","article-title":"Random subspace ensembles for fMRI classification","volume":"29","author":"Kuncheva","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.csda.2012.09.018","article-title":"Using random subspace method for prediction and variable importance assessment in linear regression","volume":"71","author":"Mielniczuk","year":"2014","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/TITS.2006.888603","article-title":"The selective random subspace predictor for traffic flow forecasting","volume":"8","author":"Sun","year":"2007","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"124602","DOI":"10.1016\/j.jhydrol.2020.124602","article-title":"Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping","volume":"583","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Li, Y., and Chen, W. (2020). Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water, 12.","DOI":"10.3390\/w12010113"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Wang, G., Lei, X., Chen, W., Shahabi, H., and Shirzadi, A. (2020). Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry, 12.","DOI":"10.3390\/sym12030325"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., and Pham, B.T. (2020). Performance evaluation of gis-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int. J. Geo-Inform., 9.","DOI":"10.3390\/ijgi9070443"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","article-title":"Gis-based landslide susceptibility assessment using optimized hybrid machine learning methods","volume":"196","author":"Chen","year":"2021","journal-title":"CATENA"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). Gis-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Appl. Sci., 10.","DOI":"10.3390\/app10010016"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"104777","DOI":"10.1016\/j.catena.2020.104777","article-title":"Gis-based evaluation of landslide susceptibility using hybrid computational intelligence models","volume":"195","author":"Chen","year":"2020","journal-title":"CATENA"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.gsf.2020.07.012","article-title":"Landslide susceptibility modeling based on anfis with teaching-learning-based optimization and satin bowerbird optimizer","volume":"12","author":"Chen","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2013.04.005","article-title":"Habitat prediction and knowledge extraction for spawning European grayling (Thymallusthymallus L.) using a broad range of species distribution models","volume":"47","author":"Fukuda","year":"2013","journal-title":"Environ. Modell. Softw."},{"key":"ref_117","unstructured":"Saltelli, A., Chan, K., and Scott, E.M. (2000). Sensitivity Analysis, Wiley."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/13658810110053125","article-title":"Uncertainty and sensitivity analysis: Tools for GISbased model implementation","volume":"15","author":"Crosetto","year":"2001","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.scitotenv.2016.02.133","article-title":"Trends in sensitivity analysis practice in the last decade","volume":"568","author":"Ferretti","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1016\/j.envsoft.2010.06.001","article-title":"Spatial sensitivity analysis of multi-criteria weights in GISbased land suitability evaluation","volume":"25","author":"Chen","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1080\/02693799008941556","article-title":"Attribute error and sensitivity analysis of map operations in geographical information systems: Suitability analysis","volume":"4","author":"Lodwick","year":"1990","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jhydrol.2010.12.027","article-title":"GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea","volume":"399","author":"Oh","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10040-014-1198-x","article-title":"Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia","volume":"23","author":"Fenta","year":"2015","journal-title":"Hydrogeol. J."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.envsoft.2013.10.001","article-title":"Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MAXENT","volume":"51","author":"Convertino","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12665-014-3442-z","article-title":"Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets","volume":"73","author":"Park","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Saha, S., Roy, J., Chen, W., Blaschke, T., and Tien Bui, D. (2020). Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran. Remote Sens., 12.","DOI":"10.3390\/rs12030475"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"104223","DOI":"10.1016\/j.catena.2019.104223","article-title":"Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling","volume":"183","author":"Arabameri","year":"2019","journal-title":"Catena"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"Catena"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Buchroithner, M., Prechtel, N., and Burghardt, D. (2014). Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based j48 decision tree classifier and its ensembles. Cartography from Pole to Pole, Springer.","DOI":"10.1007\/978-3-642-32618-9"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Kuncheva, L.I. (2004). Combining Pattern Classifiers Methods and Algorithms, Wiley.","DOI":"10.1002\/0471660264"},{"key":"ref_131","first-page":"119","article-title":"On the performance of ensemble learning for automated diagnosis of breast cancer","volume":"347","author":"Onan","year":"2015","journal-title":"Artif. Intell. Perspect. Appl."},{"key":"ref_132","unstructured":"Robinzonov, N. (2019, September 20). Advances in Boosting of Temporal and Spatial Models. Ludwig-Maximilians-Universitat M\u00fcnchen. Available online: http:\/\/edoc.ub.uni-muenchen.de\/15338\/."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1016\/j.envsoft.2011.01.003","article-title":"Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA)","volume":"26","author":"Aertsen","year":"2011","journal-title":"Environ. Model. Softw."},{"key":"ref_134","first-page":"801","article-title":"Arcing Classifiers","volume":"26","author":"Breiman","year":"1998","journal-title":"Ann. Stat."},{"key":"ref_135","first-page":"1","article-title":"RPART: Recursive Partitioning and Regression Trees","volume":"4","author":"Therneau","year":"2014","journal-title":"R Package Version"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3423\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:23:31Z","timestamp":1760178211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,18]]},"references-count":135,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12203423"],"URL":"https:\/\/doi.org\/10.3390\/rs12203423","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,18]]}}}