{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T06:55:55Z","timestamp":1777100155832,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Research, Innovation, and Digitization","award":["\u201cDD 2030\u201d PN 23 13, 2023\u20132026"],"award-info":[{"award-number":["\u201cDD 2030\u201d PN 23 13, 2023\u20132026"]}]},{"name":"Ministry of Research, Innovation, and Digitization","award":["PN 23 13 02 01"],"award-info":[{"award-number":["PN 23 13 02 01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Floods, along with other natural and anthropogenic disasters, profoundly disrupt both society and the environment. Populations residing in deltaic regions worldwide are particularly vulnerable to these threats. A prime example is the Danube Delta (DD), located in the Romanian sector of the Black Sea. This research paper aims to identify areas within the DD that are highly or very highly susceptible to flooding. To accomplish this, we employed a combination of multicriteria decision-making (AHP) and artificial intelligence (AI) techniques, including deep learning neural networks (DLNNs), support vector machines (SVMs), and multilayer perceptron (MLP). The input data comprised previously flooded regions alongside eight geographical factors. All models identified high or very high flood potential of over 65% of the studied area. The models\u2019 performance was assessed using receiver operating characteristic (ROC) analysis, demonstrating excellent outcomes evaluated by the area under the curve (AUC) exceeding 0.908. This study is significant as it lays the groundwork for implementing measures against flood impacts in the DD.<\/jats:p>","DOI":"10.3390\/w16233511","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T03:44:47Z","timestamp":1733456687000},"page":"3511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Intelligent Methods for Estimating the Flood Susceptibility in the Danube Delta, Romania"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6876-8572","authenticated-orcid":false,"given":"Romulus","family":"Costache","sequence":"first","affiliation":[{"name":"Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112 Tulcea, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anca","family":"Cr\u0103ciun","sequence":"additional","affiliation":[{"name":"Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112 Tulcea, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicu","family":"Ciobotaru","sequence":"additional","affiliation":[{"name":"Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112 Tulcea, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9840-2443","authenticated-orcid":false,"given":"Alina","family":"B\u0103rbulescu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Street, 500152 Brasov, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101900","DOI":"10.1016\/j.ejrh.2024.101900","article-title":"Flood-susceptible areas within the Yellow River Basin, China: Climate changes or socioeconomic behaviors","volume":"55","author":"Zhao","year":"2024","journal-title":"J. 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