[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121350-en":3,"doc-seo-121350-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121350,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Shoreline dynamics prediction using machine learning models - from process learning to probabilistic forecasting","Coastal zones are changing due to natural and anthropogenic forcing, making reliable shoreline-change prediction critical for coastal planning, defense design, and climate adaptation. This study evaluates machine-learning models for forecasting shoreline dynamics using synthetic data, addressing limitations of traditional approaches in adaptability, accuracy, and computational cost. By testing across a complex shoreline evolution scenario, the ConvLSTM model trained on 2D gridded data is identified as optimal for capturing shoreline evolution patterns and serving as a core component for probabilistic shoreline-position prediction. Results also indicate model choice depends on evolution complexity and target accuracy.","TYPE Original Research PUBLISHED 22 May 2025  \nDOI 10.3389/fmars.2025.1562504  \nOPEN ACCESS  \nEDITED BY  \nPavitra Kumar,  \nUniversity of Liverpool, United Kingdom  \nREVIEWED BY  \nPhilip-Neri Jayson-Quashigah,  \nHelmholtz Centre for Materials and Coastal Research (HZG), Germany  \nMd Sariful Islam,  \nMassachusetts Institute of Technology, United States  \n*CORRESPONDENCE  \nAfshar Adeli  \n [afshar.adeli@ugent.be](afshar.adeli@ugent.be);  \n [afshar.adeli@yahoo.com](afshar.adeli@yahoo.com)  \nRECEIVED 17 January 2025  \nACCEPTED 05 May 2025  \nPUBLISHED 22 May 2025  \nCITATION  \nAdeli A, Dastgheib A and Roelvink D (2025) Shoreline dynamics prediction using machine learning models: from process  \nlearning to probabilistic forecasting.  \nFront. Mar. Sci. 12:1562504 .  \ndoi: 10.3389/fmars.2025.1562504  \nCOPYRIGHT  \n© 2025 Adeli, Dastgheib and Roelvink. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nShoreline dynamics prediction using machine learning models:  \nfrom process learning to probabilistic forecasting  \nAfshar Adeli1,2*, Ali Dastgheib 1,3 and Dano Roelvink 1,4,5  \n1 Department of Water Science and Engineering, IHE Delft Institute for Water Education,  \nDelft, Netherlands, 2 Department of Civil Engineering, Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium, 3 International Marine and Dredging Company (IMDC),  \nAntwerpen, Belgium, 4 Deltares, Delft, Netherlands, 5 Department of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands  \nCoastal zones are experiencing notable changes attributed to natural and anthropogenic effects. This study investigates the potential of machine learning (ML) in predicting shoreline changes, a developing field still in its early exploration phase. Traditional methods, while insightful, have faced challenges in terms of adaptability, accuracy, and computational demands. ML, as a data-driven approach, potentially offers flexibility, computational efficiency, and can avoid the constraints associated with physics-based models. This study aims to evaluate various machine learning models ’ efficacy in predicting shoreline changes using synthetic data. Through comprehensive testing across one complex shoreline evolution scenario, this research identifies the ConvLSTM model—trained on 2D gridded data—as the optimal machine learning approach suited for addressing specific shoreline complexities and evolution patterns. This approach can learn shoreline evolution, predict it, and serve as a foundational component of a proposed method for probabilistic shoreline position prediction. Additionally, the study shows that the choice of ML model depends on the complexity of shoreline evolution and the desired level of accuracy.  \nKEYWORDS  \nshoreline dynamics, machine learning, probabilistic forecasting, shoreline evolution, shoreline prediction, shorelineS model, uncertainty quantification, coastal engineering  \n1 Introduction  \nCoastal zones, which support rich biodiversity and a large portion of the global population, are experiencing significant transformations due to both natural and human influences (He and Silliman, 2019) . Predicting shoreline changes is crucial for coastal managers and policymakers, enabling informed decisions on land use, coastal defense, and climate adaptation (Nicholls et al., 2007) . The urgency of this task has grown with climate  \nFrontiers in Marine Science 01 [frontiersin.org](frontiersin.org)  \nchange, as rising sea levels and intensified storms are expected toreshape coastlines dramatically (Nicholls and Cazenave, 2010) .  \nTraditionally, shore","cbCaikMhwPuP1lCq","https://ap.wps.com/l/cbCaikMhwPuP1lCq","pdf",26433189,1,20,"English","en",105,"# Introduction\n## Background and motivation\n## Traditional process-based shoreline models\n## Machine learning approaches in coastal science","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the need to predict shoreline changes under natural and human influences, where traditional numerical and data-driven methods face challenges in adaptability, accuracy, and computational demands.\"},{\"question\":\"How does the study evaluate machine learning models?\",\"answer\":\"It tests multiple ML models using synthetic data across one complex shoreline evolution scenario to assess how effectively they learn and predict shoreline dynamics.\"},{\"question\":\"Which machine learning model performs best and why?\",\"answer\":\"ConvLSTM trained on 2D gridded data is identified as the optimal approach because it can capture specific shoreline evolution complexities and patterns, supporting probabilistic shoreline position forecasting.\"}]","Shoreline dynamics prediction using machine learning models - 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