{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:21:44Z","timestamp":1785424904088,"version":"3.56.0"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"unspecified","delay-in-days":154,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Camb. prisms Coast. futures"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This perspective examines the application of transfer learning (TL) within deep learning (DL) frameworks for extreme water level (EWL) and spatiotemporal flood predictions. We discuss the main advantages of TL, such as enabling model transferability of pretrained DL models from\/to diverse coastal-estuarine systems and reducing computational time compared to physics-based models. These advantages can accelerate the deployment of flood prediction models in data-limited locations. We also discuss challenges and limitations that hinder accurate pattern recognition and propagation of EWLs from gauge (observation) stations to surrounding locations within model domains. These limitations include dependence on similarity in data distributions, overfitting the training data and both lag and hysteresis effects in the timing of peak water levels and flood dynamics. Lastly, we explain several misconceptions and challenges in current DL approaches that hinder EWL and spatiotemporal flood prediction, including training models exclusively on extreme conditions, assessing accuracy solely through goodness-of-fit metrics, and connecting the model\u2019s knowledge of input data with physical explanations of flood processes without an adequate context. We argue that these challenges can be addressed by prioritizing storm-relevant patterns of the input data features as well as embedding spatial propagation of EWLs in DL frameworks to mimic coastal-estuarine hydrodynamic models. Ultimately, progress toward generalizable model transferability relies on the modeler\u2019s ability to incorporate physical understanding in DL architectures, alongside continued advances in physics-informed machine learning models via soft or hard constraint approaches. There remains substantial work to establish guidelines and\/or formal procedures to develop robust, interpretable, and generalizable DL models for spatiotemporal flood prediction, thereby supporting effective flood management, mitigation, and emergency preparedness.<\/jats:p>","DOI":"10.1017\/cft.2026.10037","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:00:35Z","timestamp":1780549235000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Toward transferable models for efficient spatiotemporal flood prediction across coastal-estuarine systems"],"prefix":"10.1017","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9945-9545","authenticated-orcid":false,"given":"Samuel","family":"Daramola","sequence":"first","affiliation":[{"name":"Stevens Institute of Technology"},{"id":[{"id":"https:\/\/ror.org\/02smfhw86","id-type":"ROR","asserted-by":"publisher"}],"name":"Virginia Tech"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David F.","family":"Mu\u00f1oz","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02smfhw86","id-type":"ROR","asserted-by":"publisher"}],"name":"Virginia Tech"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaopeng","family":"Shen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04p491231","id-type":"ROR","asserted-by":"publisher"}],"name":"Pennsylvania State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,6,4]]},"container-title":["Cambridge Prisms: Coastal Futures"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S2754720526100377","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T12:12:35Z","timestamp":1781698355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S2754720526100377\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"alternative-id":["S2754720526100377"],"URL":"https:\/\/doi.org\/10.1017\/cft.2026.10037","relation":{"has-review":[{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr2","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr3","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr1","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr6","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr7","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr4","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr5","asserted-by":"object"},{"id-type":"doi","id":"10.1017\/cft.2026.10037.pr8","asserted-by":"object"}]},"ISSN":["2754-7205"],"issn-type":[{"value":"2754-7205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"\u00a9 The Author(s), 2026. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e13"}}