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

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cg.contributor.affiliationUniversity of Twenteen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.contributor.affiliationArba Minch Universityen_US
cg.contributor.donorUK Research and Innovation (UKRI) Global Challenges Research Fund (GCRF)en_US
cg.coverage.countryEthiopiaen_US
cg.coverage.iso3166-alpha2ETen_US
cg.coverage.subregionAddis Ababaen_US
cg.coverage.subregionAkaki Catchmenten_US
cg.creator.identifierAlemseged Tamiru Haile: 0000-0001-8647-2188en_US
cg.identifier.doihttps://doi.org/10.1016/j.ijdrr.2024.105169en_US
cg.identifier.iwmilibraryH053685en_US
cg.identifier.projectIWMI - D-0247en_US
cg.isijournalISI Journalen_US
cg.issn2212-4209en_US
cg.journalInternational Journal of Disaster Risk Reductionen_US
cg.reviewStatusPeer Reviewen_US
cg.volume116en_US
dc.contributor.authorAsfaw, Wegayehuen_US
dc.contributor.authorRientjes, T.en_US
dc.contributor.authorBekele, Tilaye Workuen_US
dc.contributor.authorHaile, Alemseged Tamiruen_US
dc.date.accessioned2025-03-07T10:15:53Zen_US
dc.date.available2025-03-07T10:15:53Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/173511en_US
dc.titleEstimating elements susceptible to urban flooding using multisource data and machine learningen_US
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_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2024-12-30en_US
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_US
dcterms.extent105169.en_US
dcterms.issued2025-01en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherElsevieren_US
dcterms.subjectfloodingen_US
dcterms.subjecturban areasen_US
dcterms.subjectsusceptibilityen_US
dcterms.subjectpredictionen_US
dcterms.subjectdatasetsen_US
dcterms.subjectmachine learningen_US
dcterms.subjectrainfallen_US
dcterms.subjectextreme weather eventsen_US
dcterms.subjectmodelsen_US
dcterms.typeJournal Articleen_US

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