Bridging Basins with Algorithms: Machine Learning for Scalable Flood Prediction

Bridging Basins with Algorithms: Machine Learning for Scalable Flood Prediction | 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 Bridging Basins with Algorithms: Machine Learning for Scalable Flood Prediction Ufuk Yukseler, Omer Faruk Dursun, Mete Yaganoglu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7196643/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Flood disasters are complex events influenced by numerous natural and anthropogenic factors, making accurate forecasting highly challenging. Machine learning (ML) techniques offer promising results by modeling these intricate processes. However, the most critical limitation of ML based flood forecasting lies in data availability. In basins lacking historical flood records, prediction capabilities are significantly constrained. This study aims to predict flood events in a data-scarce basin by utilizing records from 41 basins with historical flood data. Based on flood events recorded between 1950 and 2020, flood forecasts were conducted for the year 2021 in a separate basin located approximately 450 km away, selected due to its proximity and similarity. The primary objective is to assess the reliability of flood predictions in ungauged basins by comparing intra-basin and cross-basin prediction models. To this end, nine different machine learning algorithms were employed, and the results were spatially mapped. Six performance evaluation metrics were applied to assess model accuracy. The findings reveal that Gradient Boosting, a Hybrid Model, and the Random Forest algorithm achieved prediction accuracies exceeding 90%. Furthermore, the effectiveness of the cross-basin prediction approach was evaluated against traditional intra-basin models. The results indicate that machine learning offers substantial potential for improving flood prediction reliability in data-deficient regions. The Eastern Black Sea Region of Turkey, one of the most flood-prone areas in the country, was selected as the study area. This region has experienced severe flood events resulting in the loss of over 650 lives and significant economic damage. The study sheds light on how machine learning can support more effective flood forecasting and risk management, even in the absence of historical data. Machine Learning Flood Geographic İnformation System Future Flood Forecast Flood Management Full Text Cite Share Download PDF Status: Posted Version 1 posted 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-7196643","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498028622,"identity":"47fbe3df-0e57-4c63-9da6-8a276b91bd1a","order_by":0,"name":"Ufuk Yukseler","email":"","orcid":"","institution":"Inonu University: Inonu Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Ufuk","middleName":"","lastName":"Yukseler","suffix":""},{"id":498028623,"identity":"10051de6-0ea5-4c43-8e0c-024d8be434a2","order_by":1,"name":"Omer Faruk Dursun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDAC5gMGYJofRCQUEKOFLQGiRbIBpMWAFC0GB8AkETr42Zi3SVf8OZxnfH514ocHBgzy/GIH8GuRbGMrkzzbdrjY7MbbzRJAGw1nzk7Ar8Xgfo+ZZGPD4cRtN85uAGlJMLhNSMsxHjPJhj+HEzfPOLv5Bwla2A4nbuDv3UacLUC/FFs2tqUnzrjBu80iwUCCsF+AIbbxZsMf68T+/rObb/6osJHnlyagBQEkwColiFUOtu8AKapHwSgYBaNgJAEAMMNEqTD/X/kAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3923-5205","institution":"Inonu University","correspondingAuthor":true,"prefix":"","firstName":"Omer","middleName":"Faruk","lastName":"Dursun","suffix":""},{"id":498028624,"identity":"41e9974f-3787-4cae-92f7-62572e1c4e54","order_by":2,"name":"Mete Yaganoglu","email":"","orcid":"","institution":"Ataturk University: Ataturk Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Mete","middleName":"","lastName":"Yaganoglu","suffix":""}],"badges":[],"createdAt":"2025-07-23 13:02:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7196643/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7196643/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104337657,"identity":"45694d22-43d9-449a-8638-e8be3ad165d4","added_by":"auto","created_at":"2026-03-10 16:11:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2599424,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7196643/v1_covered_12506b5d-5b94-43f2-ae67-b000dc1125e0.pdf"}],"financialInterests":"","formattedTitle":"Bridging Basins with Algorithms: Machine Learning for Scalable Flood Prediction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine Learning, Flood, Geographic İnformation System, Future Flood Forecast, Flood Management","lastPublishedDoi":"10.21203/rs.3.rs-7196643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7196643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlood disasters are complex events influenced by numerous natural and anthropogenic factors, making accurate forecasting highly challenging. Machine learning (ML) techniques offer promising results by modeling these intricate processes. However, the most critical limitation of ML based flood forecasting lies in data availability. In basins lacking historical flood records, prediction capabilities are significantly constrained.\u003c/p\u003e\u003cp\u003eThis study aims to predict flood events in a data-scarce basin by utilizing records from 41 basins with historical flood data. Based on flood events recorded between 1950 and 2020, flood forecasts were conducted for the year 2021 in a separate basin located approximately 450 km away, selected due to its proximity and similarity. The primary objective is to assess the reliability of flood predictions in ungauged basins by comparing intra-basin and cross-basin prediction models.\u003c/p\u003e\u003cp\u003eTo this end, nine different machine learning algorithms were employed, and the results were spatially mapped. Six performance evaluation metrics were applied to assess model accuracy. The findings reveal that Gradient Boosting, a Hybrid Model, and the Random Forest algorithm achieved prediction accuracies exceeding 90%.\u003c/p\u003e\u003cp\u003eFurthermore, the effectiveness of the cross-basin prediction approach was evaluated against traditional intra-basin models. The results indicate that machine learning offers substantial potential for improving flood prediction reliability in data-deficient regions.\u003c/p\u003e\u003cp\u003eThe Eastern Black Sea Region of Turkey, one of the most flood-prone areas in the country, was selected as the study area. This region has experienced severe flood events resulting in the loss of over 650 lives and significant economic damage. 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