Estimating elements susceptible to urban flooding using multisource data and machine learning

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cg.contributor.affiliationUniversity of Twenteen
cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.affiliationArba Minch Universityen
cg.contributor.donorUK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF)en
cg.coverage.countryEthiopia
cg.coverage.iso3166-alpha2ET
cg.coverage.subregionAddis Ababa
cg.coverage.subregionAkaki Catchment
cg.creator.identifierAlemseged Tamiru Haile: 0000-0001-8647-2188
cg.identifier.doihttps://doi.org/10.1016/j.ijdrr.2024.105169en
cg.identifier.iwmilibraryH053685
cg.identifier.projectIWMI - D-0247
cg.isijournalISI Journalen
cg.issn2212-4209en
cg.journalInternational Journal of Disaster Risk Reductionen
cg.reviewStatusPeer Reviewen
cg.volume116en
dc.contributor.authorAsfaw, Wegayehuen
dc.contributor.authorRientjes, T.en
dc.contributor.authorBekele, Tilaye Workuen
dc.contributor.authorHaile, Alemseged Tamiruen
dc.date.accessioned2025-03-07T10:15:53Zen
dc.date.available2025-03-07T10:15:53Zen
dc.identifier.urihttps://hdl.handle.net/10568/173511
dc.titleEstimating elements susceptible to urban flooding using multisource data and machine learningen
dcterms.abstractThe accuracy of flood susceptibility prediction (FSP) could be affected by inadequate representation of flood conditioning factors (FCFs) and the approaches used to identify the most relevant FCFs. This study analyzed twenty-eight FCFs derived from open-access earth observation datasets to develop FSP model for a highly urbanized Akaki catchment, which hosts and surrounds the capital city of Ethiopia, Addis Ababa. In the study, relevant FCFs were first identified using different collinearity-based and model-integrated feature selection methods, and sequentially introduced into a machine learning model. Simulated FSPs were compared against a reference flood inventory dataset to determine the most effective selection method. Findings show that: (i) using extreme rainfall indices improved the accuracy of FSP, (ii) Mean Decrease Impurity (MDI) was found to be the most effective feature selection method, (iii) geomorphological and physiographic FCFs showed the highest and the lowest predictive power, respectively, and (iv) the quantile method outperformed other approaches in classifying the flood susceptibility map. Findings indicate that an area of 217 km2 , 43000 buildings, 163 km of paved roads and 0.54 million inhabitants are highly susceptible to flooding in the catchment. In particular, Addis Ababa contains almost 75 % of the estimated susceptible elements in only one-third of the catchment area. The results of this study provide valuable insights for urban planning and flood management, helping to reduce the socio-economic impacts of flooding and enhance urban resilience.en
dcterms.accessRightsOpen Access
dcterms.available2024-12-30
dcterms.bibliographicCitationAsfaw, Wegayehu; Rientjes, T.; Bekele, Tilaye Worku; Haile, Alemseged Tamiru. 2025. Estimating elements susceptible to urban flooding using multisource data and machine learning. International Journal of Disaster Risk Reduction, 116:105169. [doi: https://doi.org/10.1016/j.ijdrr.2024.105169]en
dcterms.extent105169.en
dcterms.issued2025-01
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherElsevieren
dcterms.subjectfloodingen
dcterms.subjecturban areasen
dcterms.subjectsusceptibilityen
dcterms.subjectpredictionen
dcterms.subjectdatasetsen
dcterms.subjectmachine learningen
dcterms.subjectrainfallen
dcterms.subjectextreme weather eventsen
dcterms.subjectmodelsen
dcterms.typeJournal Article

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