{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:46:56Z","timestamp":1777045616865,"version":"3.51.4"},"reference-count":75,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flood-susceptibility mapping (FSM) is crucial for effective flood prediction and disaster prevention. Traditional methods of modeling flood vulnerability, such as the Analytical Hierarchy Process (AHP), require weights defined by experts, while machine-learning and deep-learning approaches require extensive datasets. Remote sensing is also limited by the availability of images and weather conditions. We propose a new hybrid strategy integrating deep learning with the HEC\u2013HMS and HEC\u2013RAS physical models to overcome these challenges. In this study, we introduce a Weighted Residual U-Net (W-Res-U-Net) model based on the target of the HEC\u2013HMS and RAS physical simulation without disregarding ground truth points by using two loss functions simultaneously. The W-Res-U-Net was trained on eight sub-basins and tested on five others, demonstrating superior performance with a sensitivity of 71.16%, specificity of 91.14%, and area under the curve (AUC) of 92.95% when validated against physical simulations, as well as a sensitivity of 88.89%, specificity of 93.07%, and AUC of 95.87% when validated against ground truth points. Incorporating a \u201cSigmoid Focal Loss\u201d function and a dual-loss function improved the realism and performance of the model, achieving higher sensitivity, specificity, and AUC than HEC\u2013RAS alone. This hybrid approach significantly enhances the FSM model, especially with limited real-world data.<\/jats:p>","DOI":"10.3390\/rs16193673","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T11:08:47Z","timestamp":1727780927000},"page":"3673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Novel Hybrid Deep-Learning Approach for Flood-Susceptibility Mapping"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4018-7687","authenticated-orcid":false,"given":"Abdelkader","family":"Riche","sequence":"first","affiliation":[{"name":"Faculty of Earth Sciences, Geography and Territorial Planning, University of Sciences and Technology Houari Boumediene, BP 32 Bab Ezzouar, Algiers 16111, Algeria"},{"name":"Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, I-38123 Trento, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ammar","family":"Drias","sequence":"additional","affiliation":[{"name":"Faculty of Earth Sciences, Geography and Territorial Planning, University of Sciences and Technology Houari Boumediene, BP 32 Bab Ezzouar, Algiers 16111, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mawloud","family":"Guermoui","sequence":"additional","affiliation":[{"name":"Unit\u00e9 de Recherche Appliqu\u00e9e en Energies Renouvelables, URAER, Centre de D\u00e9veloppement des Energies Renouvelables, CDER, Zone Industrielle Bounoura, BP 88, Gharda\u00efa 47000, Algeria"},{"name":"Telecommunications and Smart Systems Laboratory, University of ZianeAchour, Djelfa 17000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5553-6709","authenticated-orcid":false,"given":"Tarek","family":"Gherib","sequence":"additional","affiliation":[{"name":"Faculty of Earth Sciences, Geography and Territorial Planning, University of Sciences and Technology Houari Boumediene, BP 32 Bab Ezzouar, Algiers 16111, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0002-0696","authenticated-orcid":false,"given":"Tayeb","family":"Boulmaiz","sequence":"additional","affiliation":[{"name":"Materials, Energy Systems Technology and Environment Laboratory, University of Ghardaia, Scientific Zone, P.O. Box 455, Ghardaia 47000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boularbah","family":"Souissi","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Bab Ezzouar, Algiers 16111, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9745-3732","authenticated-orcid":false,"given":"Farid","family":"Melgani","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, I-38123 Trento, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envsoft.2017.06.012","article-title":"A Novel Hybrid Artificial Intelligence Approach for Flood Susceptibility Assessment","volume":"95","author":"Chapi","year":"2017","journal-title":"Environ. 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