Robust LSTM-Based River Stage Prediction Under Rainfall Data Incompleteness

Robust LSTM-Based River Stage Prediction Under Rainfall Data Incompleteness | 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 LSTM-Based River Stage Prediction Under Rainfall Data Incompleteness Huei-Tau Ouyang, Jhih-Huang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6858853/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Accurate and timely prediction of river stage levels during typhoon events is critical for effective flood risk management and emergency response. This study develops a data-driven forecasting model utilizing a Long Short-Term Memory (LSTM) network to simulate the rainfall-to-water-level relationship at the Lanyang Bridge in Yilan County, Taiwan. The model leverages historical hydrometeorological data acquired from a network of rainfall observation stations and a water-level gauge, encompassing ten major typhoon events recorded between 2014 and 2022. A cross-correlation analysis was conducted to identify optimal temporal lags between rainfall and river stage response, which informed data preprocessing for the LSTM model. Notably, the developed forecasting framework does not require rainfall forecasts, relying instead on recent observed rainfall and current water level, thereby mitigating errors introduced by precipitation prediction. To address situations of missing data frequently encountered during extreme events, a masking technique was incorporated into the model, allowing the model to handle incomplete input sequences robustly. The resulting model demonstrates strong potential for short-term river stage level forecasting in data-constrained and rapidly changing conditions. river-stage prediction data incompleteness LSTM typhoon feature importance Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 10 Jun, 2025 First submitted to journal 09 Jun, 2025 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-6858853","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489770701,"identity":"b730eaa3-9e64-495f-938b-936d843ba64d","order_by":0,"name":"Huei-Tau Ouyang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAxiDn4GxAUgxE6GFDcqQbIBpYcOjHEWLwQEwRYQWc/nmY9I8NXfsNt9ubpNgqLBObJDvMcCrxbKNLU2a59iz5G13DgK1nElPbGDjwa/F4BiPmTQP2+FksxuJbRKMbYeBWng3EKHl3+Fk4xkgLf+I1cLbdtjOQAKkpYEoLWnJlnP7DidI3Ehstkg4lm7cxpb/Ab+Ww4cP3njz7bA9/4z0hzc+1FjL9jMfS8CrBQSYeBgYEhtALJBaQtECBow/GBjsiVE4CkbBKBgFIxQAAHCeRL34MeJSAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0167-5275","institution":"National I-Lan University: National Ilan University","correspondingAuthor":true,"prefix":"","firstName":"Huei-Tau","middleName":"","lastName":"Ouyang","suffix":""},{"id":489770702,"identity":"2ee301ab-0d97-4c38-8686-2feaa4aa7f0f","order_by":1,"name":"Jhih-Huang Wang","email":"","orcid":"","institution":"National I-Lan University: National Ilan University","correspondingAuthor":false,"prefix":"","firstName":"Jhih-Huang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-10 04:19:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6858853/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6858853/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87576120,"identity":"b87f63ff-fb2b-4726-981c-e0047c0e4f64","added_by":"auto","created_at":"2025-07-25 11:39:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1576474,"visible":true,"origin":"","legend":"","description":"","filename":"20250610.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6858853/v1_covered_0e1d8d8d-abc7-495b-a25c-50013f126d2c.pdf"}],"financialInterests":"","formattedTitle":"Robust LSTM-Based River Stage Prediction Under Rainfall Data Incompleteness","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"river-stage prediction, data incompleteness, LSTM, typhoon, feature importance","lastPublishedDoi":"10.21203/rs.3.rs-6858853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6858853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate and timely prediction of river stage levels during typhoon events is critical for effective flood risk management and emergency response. This study develops a data-driven forecasting model utilizing a Long Short-Term Memory (LSTM) network to simulate the rainfall-to-water-level relationship at the Lanyang Bridge in Yilan County, Taiwan. The model leverages historical hydrometeorological data acquired from a network of rainfall observation stations and a water-level gauge, encompassing ten major typhoon events recorded between 2014 and 2022. A cross-correlation analysis was conducted to identify optimal temporal lags between rainfall and river stage response, which informed data preprocessing for the LSTM model. Notably, the developed forecasting framework does not require rainfall forecasts, relying instead on recent observed rainfall and current water level, thereby mitigating errors introduced by precipitation prediction. 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