Robust Storm Surge Forecasts for Early Warning System: A Machine Learning Approach Using Adaptive Monte Carlo Bayesian Model Selection Algorithm

Robust Storm Surge Forecasts for Early Warning System: A Machine Learning Approach Using Adaptive Monte Carlo Bayesian Model Selection Algorithm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Robust Storm Surge Forecasts for Early Warning System: A Machine Learning Approach Using Adaptive Monte Carlo Bayesian Model Selection Algorithm Euan Macdonald, Enrico Tubaldi, Edoardo Patelli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3879361/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 May, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract Machine-learning based methods are increasingly employed for the prediction of storm surges and development of early warning systems for coastal flooding. The evaluation of the quality of such methods need to explicitly consider the uncertainty of the prediction, which may stem from the inaccuracy in the forecasted inputs to the model as well as from the uncertainty inherent to the model itself. Defining the range of validity of the prediction is essential for the correct application of such models. Here, a methodology is proposed for building a robust model for forecasting storm surges accounting for the relevant sources of uncertainty. The model uses as inputs the mean sea level pressure and wind velocity components at 10 m above sea level. A set of Artificial Neural Networks are used in conjunction with an adaptive Bayesian model selection process to make robust storm surge forecast predictions with associated confidence intervals. The input uncertainty, characterised by comparing hindcast data and one day forecasted data, is propagated through the model via a Monte Carlo based approach. The application of the proposed methodology is illustrated by considering 24 hour target forecast predictions of storm surges for Millport, in the Firth of Clyde, Scotland, UK. It is shown that the proposed approach improves significantly the predictive performance of existing Artificial Neural Network based models and provides a meaningful confidence interval that characterises both model and input uncertainty. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 May, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted Editorial decision: Revision requested 26 Jan, 2024 Editor assigned by journal 26 Jan, 2024 Submission checks completed at journal 19 Jan, 2024 First submitted to journal 19 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3879361","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268154339,"identity":"a6e606ca-d688-455d-b305-f491d8c6f074","order_by":0,"name":"Euan Macdonald","email":"","orcid":"","institution":"University of Strathclyde","correspondingAuthor":false,"prefix":"","firstName":"Euan","middleName":"","lastName":"Macdonald","suffix":""},{"id":268154340,"identity":"8970b8dd-1bf4-40a6-8d42-538972078b52","order_by":1,"name":"Enrico Tubaldi","email":"","orcid":"","institution":"University of Strathclyde","correspondingAuthor":false,"prefix":"","firstName":"Enrico","middleName":"","lastName":"Tubaldi","suffix":""},{"id":268154341,"identity":"6d244ccd-8eb7-4876-b201-daedb0d403ef","order_by":2,"name":"Edoardo Patelli","email":"data:image/png;base64,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","orcid":"","institution":"University of Strathclyde","correspondingAuthor":true,"prefix":"","firstName":"Edoardo","middleName":"","lastName":"Patelli","suffix":""}],"badges":[],"createdAt":"2024-01-19 17:01:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3879361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3879361/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00477-025-02993-3","type":"published","date":"2025-05-07T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82537450,"identity":"ae793fa1-1bef-4b13-8005-e40dff23d105","added_by":"auto","created_at":"2025-05-12 16:06:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1649225,"visible":true,"origin":"","legend":"","description":"","filename":"ExtremeSurgeForecastingv9E149.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3879361/v1_covered_fbcf70cc-e44a-48d6-8d6c-623eb6ed6dc0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Storm Surge Forecasts for Early Warning System: A Machine Learning Approach Using Adaptive Monte Carlo Bayesian Model Selection Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3879361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3879361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachine-learning based methods are increasingly employed for the prediction of storm surges and development of early warning systems for coastal flooding. 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