Uncertainty analysis of a machine learning-aided ensemble forecast for coastal flood maps

Uncertainty analysis of a machine learning-aided ensemble forecast for coastal flood maps | 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 Uncertainty analysis of a machine learning-aided ensemble forecast for coastal flood maps Jeremy Rohmer, S. Lecacheux, D. Idier, E. Membrado, A. G. Filippini, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8758359/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract While ensemble forecasts are widely used for river flooding, they are still in their infancy for marine flooding, one of the main difficulties being the high computing time cost of full process high-fidelity hydrodynamic numerical models. Over the past decade, machine learning-based metamodeling has proved to be an effective solution in many real-world coastal engineering cases to overcome this problem. However, the metamodel remains a statistical approximation of the “true” numerical model learned using a limited number of data. This raises the question of how much the uncertainty of the metamodel contributes to the uncertainty of the forecasts compared to that related to the ensemble of metoceanic conditions. To quantify the respective influence of the different uncertainties, we apply a global sensitivity analysis (GSA) based on a dependence measure, namely the Hilbert-Schmidt independence criterion (HSIC), to two coastal towns, Andernos and Gujan-Mestras located on the Arcachon lagoon (French Atlantic coast), and to two storms, which hit the lagoon in November 2023, i.e., Ciaran and Domingos storms. The ensemble forecast of the flood maps is computed through the combination of a dimension reduction (DR) method, using a deep-learning-based autoencoder model (AE), and Gaussian process (Gp) regression models. The application of the HSIC-based GSA approach to the cases considered in this study shows that: (1) the uncertainties related the combined DR-Gp method, i.e., the AE architecture, the completeness of the training set, and the Gp predictive uncertainty, remain minor compared to that related to the ensemble of metoceanic forcing conditions; (2) when the lead time is high, at least 2 days before the storm occurrence, the variability in the metoceanic conditions has the major impact at both towns with the total uncertainty explained by this source of uncertainty reaching up to 50%; (3) as the lead time decreases, the importance of applying a bias correction to the ensemble of metoceanic conditions increases with the total uncertainty explained reaching > 25%. Early warning system Coastal flooding Metamodel Gaussian process regression Autoencoder Global Sensitivity Analysis Full Text Additional Declarations No competing interests reported. Supplementary Files RohmeretalSERRASupplMat.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 01 Feb, 2026 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. 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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-8758359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589185910,"identity":"e25d55bd-12a6-4255-a57c-0eb5d38485ab","order_by":0,"name":"Jeremy Rohmer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACxgYGBmYQg18CzJcjQYvkDDDfmDibwFoMbhCrhbmB9+HjwhybfOPbvY9f3WAwyCfCYezGxjO3pVluu3PczDqHwcCygbAWNjZp3m2HDcxupLEZ5zD8MSDCFrCW/wbGM8BaDIjWcsDAQCKN+TFxWprZmI15tyUbSNw5xsacY0CEFsP2NsbHvNvsDPhntzF/zqkgRkszgs0mwUBYAwODPBKb+QMRGkbBKBgFo2AEAgAFtS/6+vFbfAAAAABJRU5ErkJggg==","orcid":"","institution":"Bureau de Recherches Géologiques et Minières","correspondingAuthor":true,"prefix":"","firstName":"Jeremy","middleName":"","lastName":"Rohmer","suffix":""},{"id":589185911,"identity":"9f88dac5-9258-45c0-a9bb-49976529f184","order_by":1,"name":"S. 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