Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia

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cg.contributor.affiliationBahir Dar Universityen_US
cg.contributor.affiliationInternational Water Management Instituteen_US
cg.coverage.countryEthiopiaen_US
cg.coverage.iso3166-alpha2ETen_US
cg.coverage.subregionBahir Daren_US
cg.creator.identifierSeifu Tilahun: 0000-0002-5219-4527en_US
cg.identifier.doihttps://doi.org/10.2166/hydro.2024.277en_US
cg.identifier.iwmilibraryH053133en_US
cg.isijournalISI Journalen_US
cg.issn1464-7141en_US
cg.issue9en_US
cg.journalJournal of Hydroinformaticsen_US
cg.reviewStatusPeer Reviewen_US
cg.volume26en_US
dc.contributor.authorLeggesse, E. S.en_US
dc.contributor.authorDerseh, W. A.en_US
dc.contributor.authorZimale, F. A.en_US
dc.contributor.authorTilahun, Seifu Admasuen_US
dc.contributor.authorMeshesha, M. A.en_US
dc.date.accessioned2024-09-30T21:12:03Zen_US
dc.date.available2024-09-30T21:12:03Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/152514en_US
dc.titleUrban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopiaen_US
dcterms.abstractIncreased frequency and magnitude of flooding pose a significant natural hazard to urban areas worldwide. Mapping flood hazard areas are crucial for mitigating potential damage to human life and property. However, conventional hydrodynamic approaches are hindered by their extensive data requirements and computational expenses. As an alternative solution, this paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables. We employed various ML classifiers, including decision tree (DT), random forest (RF), XGBoost (XGB), and k-nearest neighbor (kNN), to assess their performance in flood hazard mapping. Model evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). Our results demonstrated promising outcomes, with AUC values of 93% (DT), 97% (RF), 98% (XGB), and 91% (kNN) for the validation dataset. RF and XGB have slightly higher performance than DT and kNN and distance to river was the most important factor. The study highlights the potential of ML for urban flood modeling, offering reasonable accuracy and supporting early warning systems. By leveraging available geo-environmental variables, ML techniques provide valuable insights into flood hazard mapping, aiding in effective urban planning and disaster management strategies.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2024-08-28en_US
dcterms.bibliographicCitationLeggesse, E. S.; Derseh, W. A.; Zimale, F. A.; Tilahun, Seifu Admasu; Meshesha, M. A. 2024. Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia. Journal of Hydroinformatics, 26(9):2124-2145. [doi: https://doi.org/10.2166/hydro.2024.277]en_US
dcterms.extent2124-2145.en_US
dcterms.issued2024-09-01en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherIWA Publishingen_US
dcterms.subjectflash floodingen_US
dcterms.subjecturban areasen_US
dcterms.subjectweather hazardsen_US
dcterms.subjectmappingen_US
dcterms.subjectrisk managementen_US
dcterms.subjectmachine learningen_US
dcterms.subjecttechniquesen_US
dcterms.subjectmodellingen_US
dcterms.subjectland useen_US
dcterms.subjectland coveren_US
dcterms.typeJournal Articleen_US

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