{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T05:30:19Z","timestamp":1766122219365,"version":"3.48.0"},"reference-count":111,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["2024.02869.BD"],"award-info":[{"award-number":["2024.02869.BD"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["CEECIND\/03665\/2018"],"award-info":[{"award-number":["CEECIND\/03665\/2018"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>Flooding and overtopping are major concerns in coastal areas due to their potential to cause severe damage to infrastructure, economic activities, and human lives. Traditional methods for predicting these phenomena include numerical and physical models, as well as empirical formulations. However, these methods have limitations, such as the high computational costs, reliance on extensive field data, and reduced accuracy under complex conditions. Recent advances in machine learning (ML) offer new opportunities to improve predictive capabilities in coastal engineering. This paper reviews ML applications for coastal flooding and overtopping prediction, analyzing commonly used models, data sources, and preprocessing techniques. Several studies report that ML models can match or exceed the performance of traditional approaches, such as empirical EurOtop formulas or high-fidelity numerical models, particularly in controlled laboratory datasets where numerical models are computationally intensive and empirical methods show larger estimation errors. However, their advantages remain task- and data-dependent, and their generalization and interpretability may lag behind physics-based methods. This review also examines recent developments, such as hybrid approaches, real-time monitoring, and explainable artificial intelligence, which show promise in addressing these limitations and advancing the operational use of ML in coastal flooding and overtopping prediction.<\/jats:p>","DOI":"10.3390\/jmse13122384","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T15:41:12Z","timestamp":1765899672000},"page":"2384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Coastal Flooding and Overtopping with Machine Learning: Review and Future Prospects"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9244-7085","authenticated-orcid":false,"given":"Moeketsi L.","family":"Duiker","sequence":"first","affiliation":[{"name":"Department of Civil Engineering and Georesources, FEUP\u2014Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"Interdisciplinary Centre of Marine and Environmental Research of the University of Porto (CIIMAR), Avenida General Norton de Matos, s\/n, 4450-208 Matosinhos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5507-0995","authenticated-orcid":false,"given":"Victor","family":"Ramos","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Georesources, FEUP\u2014Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"Interdisciplinary Centre of Marine and Environmental Research of the University of Porto (CIIMAR), Avenida General Norton de Matos, s\/n, 4450-208 Matosinhos, Portugal"}]},{"given":"Francisco","family":"Taveira-Pinto","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Georesources, FEUP\u2014Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"Interdisciplinary Centre of Marine and Environmental Research of the University of Porto (CIIMAR), Avenida General Norton de Matos, s\/n, 4450-208 Matosinhos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3768-3314","authenticated-orcid":false,"given":"Paulo","family":"Rosa-Santos","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Georesources, FEUP\u2014Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"Interdisciplinary Centre of Marine and Environmental Research of the University of Porto (CIIMAR), Avenida General Norton de Matos, s\/n, 4450-208 Matosinhos, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.coastaleng.2019.02.001","article-title":"Coastal Flooding from Wave Overtopping and Sea Level Rise Adaptation in the Northeastern USA","volume":"150","author":"Xie","year":"2019","journal-title":"Coast. 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