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Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania

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dc.citedby5
dc.contributor.authorDiaconu D.C.en_US
dc.contributor.authorCostache R.en_US
dc.contributor.authorTowfiqul Islam A.R.M.en_US
dc.contributor.authorPandey M.en_US
dc.contributor.authorPal S.C.en_US
dc.contributor.authorMishra A.P.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorid57189031449en_US
dc.contributor.authorid55888132500en_US
dc.contributor.authorid57919747800en_US
dc.contributor.authorid57612960200en_US
dc.contributor.authorid57208776491en_US
dc.contributor.authorid57219913061en_US
dc.contributor.authorid57193547008en_US
dc.date.accessioned2025-03-03T07:42:23Z
dc.date.available2025-03-03T07:42:23Z
dc.date.issued2024
dc.description.abstractStudy region: Prahova river basin located in the central-southern region of Romania. Study focus: This study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the region: The PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.ArtNo101892
dc.identifier.doi10.1016/j.ejrh.2024.101892
dc.identifier.scopus2-s2.0-85198371094
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85198371094&doi=10.1016%2fj.ejrh.2024.101892&partnerID=40&md5=23df0c91b36fad97580ee57ddf8fb4d7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36429
dc.identifier.volume54
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology: Regional Studies
dc.titleDeveloping flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romaniaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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