{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:01:39Z","timestamp":1771005699924,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T00:00:00Z","timestamp":1663891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100013276","name":"Interreg","doi-asserted-by":"publisher","award":["SOE3\/P4\/EO868"],"award-info":[{"award-number":["SOE3\/P4\/EO868"]}],"id":[{"id":"10.13039\/100013276","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/GES-AMB\/30052\/2017"],"award-info":[{"award-number":["PTDC\/GES-AMB\/30052\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["CEECIND\/00268\/2017"],"award-info":[{"award-number":["CEECIND\/00268\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["MIT-EXPL\/CS\/0018\/2019"],"award-info":[{"award-number":["MIT-EXPL\/CS\/0018\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Earth Sci"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s12665-022-10589-1","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T01:02:43Z","timestamp":1663894963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["The role of susceptibility, exposure and vulnerability as drivers of flood disaster risk at the parish level"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9785-0180","authenticated-orcid":false,"given":"Pedro Pinto","family":"Santos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9674-0964","authenticated-orcid":false,"given":"Susana","family":"Pereira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7228-6330","authenticated-orcid":false,"given":"Jorge","family":"Rocha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8367-1835","authenticated-orcid":false,"given":"Eus\u00e9bio","family":"Reis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3028-2937","authenticated-orcid":false,"given":"M\u00f3nica","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0883-8564","authenticated-orcid":false,"given":"S\u00e9rgio Cruz","family":"Oliveira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1036-6271","authenticated-orcid":false,"given":"Ricardo A. C.","family":"Garcia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8111-8777","authenticated-orcid":false,"given":"Raquel","family":"Melo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3953-673X","authenticated-orcid":false,"given":"Jos\u00e9 Lu\u00eds","family":"Z\u00eazere","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,23]]},"reference":[{"key":"10589_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2020.106620","volume":"117","author":"SA Ali","year":"2020","unstructured":"Ali SA, Parvin F, Pham QB, Vojtek M, Vojtekov\u00e1 J, Costache R, Thi Thuy Linh N, Nguyen HQ, Ahmad A, Ghorbani MA (2020) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, na\u00efve bayes tree, bivariate statistics and logistic regression: a case of Top\u013ea basin. Slovakia Ecol Indic 117:106620. https:\/\/doi.org\/10.1016\/j.ecolind.2020.106620","journal-title":"Slovakia Ecol Indic"},{"key":"10589_CR2","doi-asserted-by":"publisher","unstructured":"Arabameri A, Saha S, Chen W, Roy J, Pradhan B, Bui DT. (2020) Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques. J Hydrol 587:125007. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125007. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169420304674","DOI":"10.1016\/j.jhydrol.2020.125007"},{"key":"10589_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1051\/e3sconf\/20160711009","volume":"7","author":"A Assteerawatt","year":"2016","unstructured":"Assteerawatt A, Tsaknias D, Azemar F, Ghosh S, Hilberts A (2016) Large-scale and high-resolution flood risk model for Japan. E3S Web Conf 7:1\u20135. https:\/\/doi.org\/10.1051\/e3sconf\/20160711009","journal-title":"E3S Web Conf"},{"key":"10589_CR4","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and regression trees","author":"L Breiman","year":"2017","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ (2017) Classification and regression trees. Routledge, London"},{"key":"10589_CR5","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.scitotenv.2019.02.422","volume":"668","author":"DT Bui","year":"2019","unstructured":"Bui DT, Tsangaratos P, Ngo P-TT, Pham TD, Pham BT (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038\u20131054. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.02.422","journal-title":"Sci Total Environ"},{"key":"10589_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/su8090948","author":"C Cao","year":"2016","unstructured":"Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustain. https:\/\/doi.org\/10.3390\/su8090948","journal-title":"Sustain"},{"key":"10589_CR7","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.envsoft.2017.06.012","volume":"95","author":"K Chapi","year":"2017","unstructured":"Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229\u2013245","journal-title":"Environ Model Softw"},{"issue":"4","key":"10589_CR8","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s13753-013-0018-6","volume":"4","author":"W Chen","year":"2013","unstructured":"Chen W, Cutter SL, Emrich CT, Shi P (2013) Measuring social vulnerability to natural hazards in the Yangtze River Delta region. China Int J Disaster Risk Sci 4(4):169\u2013181. https:\/\/doi.org\/10.1007\/s13753-013-0018-6","journal-title":"China Int J Disaster Risk Sci"},{"key":"10589_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134979","volume":"701","author":"W Chen","year":"2020","unstructured":"Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Wang X, Bian H, Zhang S, Pradhan B et al (2020) Modeling flood susceptibility using data-driven approaches of na\u00efve Bayes tree, alternating decision tree, and random forest methods. Sci Total Environ 701:134979. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.134979","journal-title":"Sci Total Environ"},{"issue":"8","key":"10589_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12665-018-7498-z","volume":"77","author":"B Choubin","year":"2018","unstructured":"Choubin B, Zehtabian G, Azareh A, Rafiei-Sardooi E, Sajedi-Hosseini F, Ki\u015fi \u00d6 (2018) Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches. Environ Earth Sci 77(8):1\u201313","journal-title":"Environ Earth Sci"},{"issue":"Pt 2","key":"10589_CR11","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1016\/j.scitotenv.2018.10.064","volume":"651","author":"B Choubin","year":"2019","unstructured":"Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651(Pt 2):2087\u20132096. https:\/\/doi.org\/10.1016\/j.scitotenv.2018.10.064","journal-title":"Sci Total Environ"},{"key":"10589_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134514","volume":"711","author":"R Costache","year":"2020","unstructured":"Costache R, Hong H, Pham QB (2020a) Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. Sci Total Environ 711:134514. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.134514","journal-title":"Sci Total Environ"},{"key":"10589_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/rs12010106","author":"R Costache","year":"2020","unstructured":"Costache R, Pham QB, Sharifi E, Linh NT, Abba SI, Vojtek M, Vojtekov\u00e1 J, Nhi PT, Khoi DN (2020b) Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sens. https:\/\/doi.org\/10.3390\/rs12010106","journal-title":"Remote Sens"},{"key":"10589_CR14","doi-asserted-by":"crossref","unstructured":"Costache R, Popa MC, Tien Bui D, Diaconu DC, Ciubotaru N, Minea G, Pham QB. (2020c) Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. J Hydrol. 585:124808. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169420302687","DOI":"10.1016\/j.jhydrol.2020.124808"},{"key":"10589_CR15","unstructured":"CRED, UNDRR (2020) Human cost of disasters. An overview of the last 20 years (2000\u20132019). Geneva, Switzerland. https:\/\/reliefweb.int\/report\/world\/human-cost-disasters-overview-last-20-years-2000-2019"},{"issue":"2","key":"10589_CR16","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1111\/1540-6237.8402002","volume":"84","author":"SL Cutter","year":"2003","unstructured":"Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Q 84(2):242\u2013261. https:\/\/doi.org\/10.1111\/1540-6237.8402002","journal-title":"Soc Sci Q"},{"key":"10589_CR17","doi-asserted-by":"publisher","unstructured":"De Groeve T, Poljansek K, Ehrlich D. (2013) Recording disaster losses recommendations for a European approach. http:\/\/dx.publications.europa.eu\/https:\/\/doi.org\/10.2788\/98653","DOI":"10.2788\/98653"},{"issue":"7","key":"10589_CR18","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.5194\/nhess-17-1111-2017","volume":"17","author":"F Dottori","year":"2017","unstructured":"Dottori F, Kalas M, Salamon P, Bianchi A, Alfieri L, Feyen L (2017) An operational procedure for rapid flood risk assessment in Europe. Nat Hazards Earth Syst Sci 17(7):1111\u20131126. https:\/\/doi.org\/10.5194\/nhess-17-1111-2017","journal-title":"Nat Hazards Earth Syst Sci"},{"issue":"2","key":"10589_CR19","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s10346-012-0320-1","volume":"10","author":"\u00c1M Felic\u00edsimo","year":"2013","unstructured":"Felic\u00edsimo \u00c1M, Cuartero A, Remondo J, Quir\u00f3s E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10(2):175\u2013189. https:\/\/doi.org\/10.1007\/s10346-012-0320-1","journal-title":"Landslides"},{"issue":"6","key":"10589_CR20","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1175\/2008BAMS2721.1","volume":"90","author":"M Gall","year":"2009","unstructured":"Gall M, Borden KA, Cutter SL (2009) When do losses count? Bull Am Meteorol Soc 90(6):799\u2013809. https:\/\/doi.org\/10.1175\/2008BAMS2721.1","journal-title":"Bull Am Meteorol Soc"},{"issue":"2","key":"10589_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1029\/2010JD014255","volume":"116","author":"MC Gallego","year":"2011","unstructured":"Gallego MC, Trigo RM, Vaquero JM, Brunet M, Garc\u00eda JA, Sigr\u00f3 J, Valente MA (2011) Trends in frequency indices of daily precipitation over the Iberian Peninsula during the last century. J Geophys Res Atmos 116(2):1\u201318. https:\/\/doi.org\/10.1029\/2010JD014255","journal-title":"J Geophys Res Atmos"},{"key":"10589_CR22","doi-asserted-by":"publisher","unstructured":"Ghosh A, Kar SK. (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Nat Hazards J Int Soc Prev Mitig Nat Hazards. 94(1):349\u2013368. https:\/\/doi.org\/10.1007\/s11069-018-3392-y. https:\/\/ideas.repec.org\/a\/spr\/nathaz\/v94y2018i1d10.1007_s11069-018-3392-y.html","DOI":"10.1007\/s11069-018-3392-y"},{"key":"10589_CR23","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.scitotenv.2017.12.256","volume":"625","author":"H Hong","year":"2018","unstructured":"Hong H, Tsangaratos P, Ilia I, Liu J, Zhu A-X, Chen W (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\u2013588. https:\/\/doi.org\/10.1016\/j.scitotenv.2017.12.256","journal-title":"China Sci Total Environ"},{"issue":"8","key":"10589_CR24","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.1007\/s00477-013-0716-z","volume":"27","author":"Z Ji","year":"2013","unstructured":"Ji Z, Li N, Xie W, Wu J, Zhou Y (2013) Comprehensive assessment of flood risk using the classification and regression tree method. Stoch Environ Res Risk Assess 27(8):1815\u20131828. https:\/\/doi.org\/10.1007\/s00477-013-0716-z","journal-title":"Stoch Environ Res Risk Assess"},{"issue":"12","key":"10589_CR25","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1007\/s10661-016-5665-9","volume":"188","author":"K Khosravi","year":"2016","unstructured":"Khosravi K, Pourghasemi HR, Chapi K, Bahri M (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(12):656. https:\/\/doi.org\/10.1007\/s10661-016-5665-9","journal-title":"Environ Monit Assess"},{"key":"10589_CR26","doi-asserted-by":"publisher","unstructured":"Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly H-B, Gr\u00f3f G, Ho HL et al. (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol. 573:311\u2013323. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.03.073. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169419303026","DOI":"10.1016\/j.jhydrol.2019.03.073"},{"key":"10589_CR27","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.envsci.2014.10.013","volume":"47","author":"EE Koks","year":"2015","unstructured":"Koks EE, Jongman B, Husby TG, Botzen WJW (2015) Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environ Sci Policy 47:42\u201352. https:\/\/doi.org\/10.1016\/j.envsci.2014.10.013","journal-title":"Environ Sci Policy"},{"issue":"3","key":"10589_CR28","doi-asserted-by":"publisher","first-page":"535","DOI":"10.5194\/nhess-12-535-2012","volume":"12","author":"W Kron","year":"2012","unstructured":"Kron W, Steuer M, L\u00f6w P, Wirtz A (2012) How to deal properly with a natural catastrophe database\u2014analysis of flood losses. Nat Hazards Earth Syst Sci 12(3):535\u2013550. https:\/\/doi.org\/10.5194\/nhess-12-535-2012","journal-title":"Nat Hazards Earth Syst Sci"},{"issue":"2","key":"10589_CR29","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1080\/19475705.2017.1308971","volume":"8","author":"S Lee","year":"2017","unstructured":"Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Nat Hazards Risk 8(2):1185\u20131203. https:\/\/doi.org\/10.1080\/19475705.2017.1308971","journal-title":"Geomatics, Nat Hazards Risk"},{"issue":"2","key":"10589_CR30","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s11069-012-0180-y","volume":"63","author":"K Li","year":"2012","unstructured":"Li K, Wu S, Dai E, Xu Z (2012) Flood loss analysis and quantitative risk assessment in China. Nat Hazards 63(2):737\u2013760. https:\/\/doi.org\/10.1007\/s11069-012-0180-y","journal-title":"Nat Hazards"},{"key":"10589_CR31","doi-asserted-by":"crossref","unstructured":"Luu C, Meding J Von, Kanjanabootra S. (2018) Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam. Nat Hazards. 90(3):1031\u20131050. 101007\/s11069-017-3083-0. http:\/\/inis.iaea.org\/search\/search.aspx?orig_q=RN:51110605","DOI":"10.1007\/s11069-017-3083-0"},{"key":"10589_CR32","doi-asserted-by":"publisher","unstructured":"Mahjoobi J, Etemad-Shahidi A. (2008) An alternative approach for the prediction of significant wave heights based on classification and regression trees. Appl Ocean Res. 30(3):172\u2013177. https:\/\/doi.org\/10.1016\/j.apor.2008.11.001. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0141118708000680","DOI":"10.1016\/j.apor.2008.11.001"},{"key":"10589_CR33","doi-asserted-by":"publisher","unstructured":"Mazzoleni M, M\u00e5rd J, Rusca M, Odongo V, Lindersson S, Di Baldassarre G. (2020) Floodplains in the anthropocene: a global analysis of the interplay between human population, built environment and flood severity. Water Resour Res. 57(2):e2020WR027744. https:\/\/onlinelibrary.wiley.com. https:\/\/doi.org\/10.1029\/2020WR027744. Accessed 15 Dec 2020","DOI":"10.1029\/2020WR027744"},{"issue":"2","key":"10589_CR34","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/s0269-7491(01)00111-7","volume":"115","author":"CD McLay","year":"2001","unstructured":"McLay CD, Dragten R, Sparling G, Selvarajah N (2001) Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches. Environ Pollut 115(2):191\u2013204. https:\/\/doi.org\/10.1016\/s0269-7491(01)00111-7","journal-title":"Environ Pollut"},{"issue":"1","key":"10589_CR35","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1080\/13669870802447962","volume":"12","author":"JM Mendes","year":"2009","unstructured":"Mendes JM (2009) Social vulnerability indexes as planning tools: beyond the preparedness paradigm. J Risk Res 12(1):43\u201358. https:\/\/doi.org\/10.1080\/13669870802447962","journal-title":"J Risk Res"},{"issue":"1","key":"10589_CR36","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1108\/IJDRBE-10-2019-0069","volume":"11","author":"JM Mendes","year":"2019","unstructured":"Mendes JM, Tavares AO, Santos PP (2019) Social vulnerability and local level assessments: a new approach for planning. Int J Disaster Resil Built Environ 11(1):15\u201343. https:\/\/doi.org\/10.1108\/IJDRBE-10-2019-0069","journal-title":"Int J Disaster Resil Built Environ"},{"key":"10589_CR37","doi-asserted-by":"publisher","unstructured":"Miles RE, Snow CC. (1984) Designing strategic human resources systems. Organ Dyn. 13(1):36\u201352. https:\/\/doi.org\/10.1016\/0090-2616(84)90030-5. https:\/\/www.sciencedirect.com\/science\/article\/pii\/0090261684900305","DOI":"10.1016\/0090-2616(84)90030-5"},{"key":"10589_CR38","unstructured":"Miranda PMA, Esp\u00edrito F, Coelho S, Rodrigues TA, Valente MA, Pires HO, Pires VC, Miranda PMA, Coelho FES, Tom\u00e9 AR et al. (2002) 20th century portuguese climate and climate scenarios. In: Santos FD, Forbes K, Moita R (Eds). Climate change in portugal: scenarios, impacts and adaptation measures (SIAM Project). Gradiva, Lisbon, Portugal, p 23\u201383"},{"issue":"3","key":"10589_CR39","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/s13753-019-0224-y","volume":"10","author":"RI Ogie","year":"2019","unstructured":"Ogie RI, Pradhan B (2019) Natural hazards and social vulnerability of place: the strength-based approach applied to Wollongong, Australia. Int J Disaster Risk Sci 10(3):404\u2013420. https:\/\/doi.org\/10.1007\/s13753-019-0224-y","journal-title":"Int J Disaster Risk Sci"},{"key":"10589_CR75","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.ijdrr.2017.03.007","volume":"22","author":"S Pereira","year":"2017","unstructured":"Pereira S, Diakakis M, Deligiannakis G, Z\u00eazere JL (2017) Comparing flood mortality in Portugal and Greece (western and eastern mediterranean). Int J Disaster Risk Reduct 22:147\u2013157. https:\/\/doi.org\/10.1016\/j.ijdrr.2017.03.007","journal-title":"Int J Disaster Risk Reduct"},{"key":"10589_CR40","doi-asserted-by":"publisher","first-page":"191","DOI":"10.3389\/FENVS.2019.00191\/BIBTEX","volume":"7","author":"K Phongsapan","year":"2019","unstructured":"Phongsapan K, Chishtie F, Poortinga A, Bhandari B, Meechaiya C, Kunlamai T, Aung KS, Saah D, Anderson E, Markert K et al (2019) Operational flood risk index mapping for disaster risk reduction using earth observations and cloud computing technologies: a case study on Myanmar. Front Environ Sci 7:191. https:\/\/doi.org\/10.3389\/FENVS.2019.00191\/BIBTEX","journal-title":"Front Environ Sci"},{"issue":"2","key":"10589_CR42","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","volume":"9","author":"AM Prasad","year":"2006","unstructured":"Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2):181\u2013199","journal-title":"Ecosystems"},{"key":"10589_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/feart.2014.00025","volume":"2","author":"AM Ramos","year":"2014","unstructured":"Ramos AM, Cortesi N, Trigo RM (2014) Circulation weather types and spatial variability of daily precipitation in the Iberian Peninsula. Front Earth Sci 2:1\u201317. https:\/\/doi.org\/10.3389\/feart.2014.00025","journal-title":"Front Earth Sci"},{"key":"10589_CR43","doi-asserted-by":"publisher","unstructured":"Ramos AM, Trigo RM, Liberato MLR, Tom\u00e9 R. (2015) Daily precipitation extreme events in the iberian peninsula and its association with atmospheric rivers. J Hydrometeorol. 16(2):579\u2013597. https:\/\/doi.org\/10.1175\/JHM-D-14-0103.1. https:\/\/journals.ametsoc.org\/view\/journals\/hydr\/16\/2\/jhm-d-14-0103_1.xml Accessed 31 Mar 2022","DOI":"10.1175\/JHM-D-14-0103.1"},{"key":"10589_CR44","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.scitotenv.2017.09.262","volume":"615","author":"SV Razavi Termeh","year":"2018","unstructured":"Razavi Termeh SV, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438\u2013451. https:\/\/doi.org\/10.1016\/j.scitotenv.2017.09.262","journal-title":"Sci Total Environ"},{"issue":"1","key":"10589_CR45","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s40808-018-0427-z","volume":"4","author":"RK Samanta","year":"2018","unstructured":"Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR (2018) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4(1):395\u2013408. https:\/\/doi.org\/10.1007\/s40808-018-0427-z","journal-title":"Model Earth Syst Environ"},{"key":"10589_CR73","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.ijdrr.2014.08.003.10.1016\/j.ijdrr.2014.08.003","volume":"639","author":"M Santos","year":"2014","unstructured":"Santos M, Bateira C, Soares L, Hermenegildo C (2014) Hydrogeomorphologic GIS database in Northern Portugal, between 1865 and 2010: temporal and spatial analysis. Int J Disaster Risk Reduction 10:143\u2013152. https:\/\/doi.org\/10.1016\/j.ijdrr.2014.08.003.10.1016\/j.ijdrr.2014.08.003","journal-title":"Int J Disaster Risk Reduction"},{"key":"10589_CR74","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.jhydrol.2015.10.011","volume":"530","author":"M Santos","year":"2015","unstructured":"Santos M, Santos JA, Fragoso M (2015) Historical damaging flood records for 1871-2011 in Northern Portugal and underlying atmospheric forcings. J Hydrol 530:591\u2013603","journal-title":"J Hydrol"},{"key":"10589_CR46","doi-asserted-by":"publisher","DOI":"10.1111\/jfr3.12290","author":"PP Santos","year":"2018","unstructured":"Santos PP, Reis E (2018) Assessment of stream flood susceptibility: a cross-analysis between model results and flood losses. J Flood Risk Manag. https:\/\/doi.org\/10.1111\/jfr3.12290","journal-title":"J Flood Risk Manag"},{"issue":"3","key":"10589_CR47","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s11069-017-3166-y","volume":"91","author":"M Santos","year":"2018","unstructured":"Santos M, Fragoso M, Santos JA (2018) Damaging flood severity assessment in Northern Portugal over more than 150 years (1865\u20132016). Nat Hazards 91(3):983\u20131002. https:\/\/doi.org\/10.1007\/s11069-017-3166-y","journal-title":"Nat Hazards"},{"key":"10589_CR48","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.scitotenv.2019.02.328","volume":"667","author":"PP Santos","year":"2019","unstructured":"Santos PP, Reis E, Pereira S, Santos M (2019) A flood susceptibility model at the national scale based on multicriteria analysis. Sci Total Environ 667:325\u2013337. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.02.328","journal-title":"Sci Total Environ"},{"key":"10589_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2020.110127","author":"PP Santos","year":"2020","unstructured":"Santos PP, Pereira S, Z\u00eazere JL, Tavares AO, Reis E, Garcia RAC, Oliveira SC (2020) A comprehensive approach to understanding flood risk drivers at the municipal level. J Environ Manage. https:\/\/doi.org\/10.1016\/j.jenvman.2020.110127","journal-title":"J Environ Manage"},{"issue":"9","key":"10589_CR50","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1080\/10106049.2017.1316780","volume":"33","author":"S Siahkamari","year":"2018","unstructured":"Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O (2018) Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto Int 33(9):927\u2013941. https:\/\/doi.org\/10.1080\/10106049.2017.1316780","journal-title":"Geocarto Int"},{"issue":"9","key":"10589_CR51","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1080\/10106049.2019.1566405","volume":"35","author":"D Souissi","year":"2020","unstructured":"Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2020) GIS-based MCDM\u2013AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int 35(9):991\u20131017. https:\/\/doi.org\/10.1080\/10106049.2019.1566405","journal-title":"Geocarto Int"},{"key":"10589_CR52","doi-asserted-by":"publisher","unstructured":"Tang Z, Zhang H, Yi S, Xiao Y. (2018) Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis. J Hydrol. 558:144\u2013158. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.01.033. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169418300349","DOI":"10.1016\/j.jhydrol.2018.01.033"},{"key":"10589_CR53","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.ijdrr.2018.07.009","volume":"31","author":"AO Tavares","year":"2018","unstructured":"Tavares AO, Barros JL, Mendes JM, Santos PP, Pereira S (2018) Decennial comparison of changes in social vulnerability: a municipal analysis in support of risk management. Int J Disaster Risk Reduct 31:679\u2013690. https:\/\/doi.org\/10.1016\/j.ijdrr.2018.07.009","journal-title":"Int J Disaster Risk Reduct"},{"key":"10589_CR54","doi-asserted-by":"publisher","unstructured":"Tehrany MS, Pradhan B, Jebur MN. (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\u201379. https:\/\/doi.org\/10.1016\/j.jhydrol.2013.09.034. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022169413006872","DOI":"10.1016\/j.jhydrol.2013.09.034"},{"key":"10589_CR55","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.jhydrol.2014.03.008","volume":"512","author":"MS Tehrany","year":"2014","unstructured":"Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332\u2013343. https:\/\/doi.org\/10.1016\/j.jhydrol.2014.03.008","journal-title":"J Hydrol"},{"key":"10589_CR56","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.scitotenv.2017.09.262","volume":"615","author":"SVR Termeh","year":"2018","unstructured":"Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438\u2013451","journal-title":"Sci Total Environ"},{"key":"10589_CR57","doi-asserted-by":"publisher","DOI":"10.3390\/rs11131589","author":"D Tien Bui","year":"2019","unstructured":"Tien Bui D, Khosravi K, Shahabi H, Daggupati P, Adamowski JF, Melesse AM, Thai Pham B, Pourghasemi HR, Mahmoudi M, Bahrami S et al (2019) Flood spatial modeling in Northern Iran using remote sensing and GIS: a comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sens. https:\/\/doi.org\/10.3390\/rs11131589","journal-title":"Remote Sens"},{"key":"10589_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134413","volume":"701","author":"D Tien Bui","year":"2020","unstructured":"Tien Bui D, Hoang N-D, Mart\u00ednez-\u00c1lvarez F, Ngo P-TT, Hoa PV, Pham TD, Samui P, Costache R (2020) A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. Sci Total Environ 701:134413. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.134413","journal-title":"Sci Total Environ"},{"key":"10589_CR59","first-page":"1","volume-title":"Classification and regression trees (CART) theory and applications","author":"R Timofeev","year":"2004","unstructured":"Timofeev R (2004) Classification and regression trees (CART) theory and applications. Humboldt Univ, Berlin, pp 1\u201340"},{"issue":"2","key":"10589_CR60","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/S1462-0758(00)00010-8","volume":"1","author":"E Todini","year":"1999","unstructured":"Todini E (1999) An operational decision support system for flood risk mapping, forecasting and management. Urban Water 1(2):131\u2013143. https:\/\/doi.org\/10.1016\/S1462-0758(00)00010-8","journal-title":"Urban Water"},{"issue":"13","key":"10589_CR61","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1002\/1097-0088(20001115)20:13<1559::AID-JOC555>3.0.CO;2-5","volume":"20","author":"RM Trigo","year":"2000","unstructured":"Trigo RM, DaCamara CC (2000) Circulation weather types and their influence on the precipitation regime in Portugal. Int J Climatol 20(13):1559\u20131581. https:\/\/doi.org\/10.1002\/1097-0088(20001115)20:13%3c1559::AID-JOC555%3e3.0.CO;2-5","journal-title":"Int J Climatol"},{"key":"10589_CR62","unstructured":"UNDRR (2019) Global assessment report on disaster risk reduction (GAR2019). Geneva, Switzerland: United Nations Office for Disaster Risk Reduction. https:\/\/gar.undrr.org\/report-2019"},{"key":"10589_CR63","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1016\/j.jenvman.2019.06.102","volume":"247","author":"Y Wang","year":"2019","unstructured":"Wang Y, Hong H, Chen W, Li S, Panahi M, Khosravi K, Shirzadi A, Shahabi H, Panahi S, Costache R (2019) Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. J Environ Manage 247:712\u2013729. https:\/\/doi.org\/10.1016\/j.jenvman.2019.06.102","journal-title":"J Environ Manage"},{"issue":"8","key":"10589_CR64","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1038\/nclimate2742","volume":"5","author":"PJ Ward","year":"2015","unstructured":"Ward PJ, Jongman B, Salamon P, Simpson A, Bates P, De Groeve T, Muis S, De Perez EC, Rudari R, Trigg MA et al (2015) Usefulness and limitations of global flood risk models. Nat Clim Chang 5(8):712\u2013715. https:\/\/doi.org\/10.1038\/nclimate2742","journal-title":"Nat Clim Chang"},{"key":"10589_CR65","doi-asserted-by":"publisher","unstructured":"Ward PJ, Jongman B, Weiland FS, Bouwman A, Van Beek R, Bierkens MFP, Ligtvoet W, Winsemius HC. (2013) Assessing flood risk at the global scale: model setup, results, and sensitivity. Environ Res Lett. 8(4):044019. https:\/\/iopscience.iop.org\/article\/https:\/\/doi.org\/10.1088\/1748-9326\/8\/4\/044019. Accessed 15 Mar 2022","DOI":"10.1088\/1748-9326\/8\/4\/044019"},{"key":"10589_CR66","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/aaac65","author":"OEJ Wing","year":"2018","unstructured":"Wing OEJ, Bates PD, Smith AM, Sampson CC, Johnson KA, Fargione J, Morefield P (2018) Estimates of present and future flood risk in the conterminous United States. Environ Res Lett. https:\/\/doi.org\/10.1088\/1748-9326\/aaac65","journal-title":"Environ Res Lett"},{"issue":"2","key":"10589_CR67","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s11069-012-0189-2","volume":"63","author":"DK Yoon","year":"2012","unstructured":"Yoon DK (2012) Assessment of social vulnerability to natural disasters: a comparative study. Nat Hazards 63(2):823\u2013843. https:\/\/doi.org\/10.1007\/s11069-012-0189-2","journal-title":"Nat Hazards"},{"issue":"3","key":"10589_CR68","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1080\/19475705.2015.1012750","volume":"7","author":"AM Youssef","year":"2016","unstructured":"Youssef AM, Sefry SA, Pradhan B, Alfadail EA (2016) Analysis on causes of flash flood in Jeddah city (Kingdom of Saudi Arabia) of 2009 and 2011 using multi-sensor remote sensing data and GIS. Geomatics, Nat Hazards Risk 7(3):1018\u20131042. https:\/\/doi.org\/10.1080\/19475705.2015.1012750","journal-title":"Geomatics, Nat Hazards Risk"},{"issue":"2","key":"10589_CR69","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/s11069-013-1018-y","volume":"72","author":"JL Z\u00eazere","year":"2014","unstructured":"Z\u00eazere JL, Pereira S, Tavares AO, Bateira C, Trigo RM, Quaresma I, Santos PP, Santos M, Verde J (2014) DISASTER: A GIS database on hydro-geomorphologic disasters in Portugal. Nat Hazards 72(2):503\u2013532. https:\/\/doi.org\/10.1007\/s11069-013-1018-y","journal-title":"Nat Hazards"},{"key":"10589_CR70","doi-asserted-by":"publisher","unstructured":"Zhao G, Pang B, Xu Z, Yue J, Tu T. (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ. 615:1133\u20131142. https:\/\/doi.org\/10.1016\/j.scitotenv.2017.10.037. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0048969717327419","DOI":"10.1016\/j.scitotenv.2017.10.037"},{"key":"10589_CR71","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.scitotenv.2018.05.056","volume":"639","author":"AP Zischg","year":"2018","unstructured":"Zischg AP, Hofer P, Mosimann M, R\u00f6thlisberger V, Ramirez JA, Keiler M, Weingartner R (2018) Flood risk (d)evolution: disentangling key drivers of flood risk change with a retro-model experiment. Sci Total Environ 639:195\u2013207. https:\/\/doi.org\/10.1016\/j.scitotenv.2018.05.056","journal-title":"Sci Total Environ"}],"container-title":["Environmental Earth Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12665-022-10589-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12665-022-10589-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12665-022-10589-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T07:16:15Z","timestamp":1665213375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12665-022-10589-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,23]]},"references-count":74,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["10589"],"URL":"https:\/\/doi.org\/10.1007\/s12665-022-10589-1","relation":{},"ISSN":["1866-6280","1866-6299"],"issn-type":[{"value":"1866-6280","type":"print"},{"value":"1866-6299","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,23]]},"assertion":[{"value":"14 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"465"}}