{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T18:28:31Z","timestamp":1777832911316,"version":"3.51.4"},"reference-count":115,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T00:00:00Z","timestamp":1712707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon Europe research and innovation programme","award":["101135410"],"award-info":[{"award-number":["101135410"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.<\/jats:p>","DOI":"10.3390\/jmse12040638","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T06:07:46Z","timestamp":1712729266000},"page":"638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3261-6523","authenticated-orcid":false,"given":"Mahmoud","family":"Abouhalima","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, FEUP\u2014Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"},{"name":"Construction & Building Engineering Department, College of Engineering and Technology, Arab Academy for Science Technology and Maritime Transport, Alexandria P.O. Box 1029, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7876-6423","authenticated-orcid":false,"given":"Luciana","family":"das Neves","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, 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 (CIIMAR), Leix\u00f5es Cruise Terminal, Av. General Norton de Matos s\/n, 4450-208 Matosinhos, Portugal"},{"name":"IMDC\u2014International Marine and Dredging Consultants, Van Immerseelstraat 66, 2018 Antwerp, Belgium"}]},{"given":"Francisco","family":"Taveira-Pinto","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, 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 (CIIMAR), Leix\u00f5es Cruise Terminal, Av. 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, 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 (CIIMAR), Leix\u00f5es Cruise Terminal, Av. General Norton de Matos s\/n, 4450-208 Matosinhos, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1017\/cft.2022.4","article-title":"The future of coastal monitoring through satellite remote sensing","volume":"1","author":"Vitousek","year":"2022","journal-title":"Camb. Prism. Coast. 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