Baseflow Separation for Improving Dam Inflow Prediction using Data-Driven Models

Baseflow Separation for Improving Dam Inflow Prediction using Data-Driven Models | 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 Baseflow Separation for Improving Dam Inflow Prediction using Data-Driven Models Heechan Han, Heeseung Park, donghyun kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4316281/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2025 Read the published version in Water Resources Management → Version 1 posted 5 You are reading this latest preprint version Abstract Improving the accuracy of rainfall-runoff simulations is an important challenge for efficient water resource management. Data-driven models are alternatives for simulating and predicting streamflows based on the relationships between meteorological variables and runoff. To improve runoff forecasting performance, we present data-driven model-based runoff forecasting algorithms coupled with a baseflow separation process. For the evaluation, we used two types of data-driven algorithms, deep neural network (DNN) and random forest (RF), and considered the historical patterns of precipitation, air temperature, humidity, and dam inflows as input data for the algorithms. In addition, we evaluated the prediction model by applying lead times of 1–7 days to construct the optimal input datasets. The performance of the dam inflow prediction using data-driven models coupled with the baseflow separation process was better than that of the algorithm without the process. Baseflow separation Dam inflow Deep neural network Random forest Full Text Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2025 Read the published version in Water Resources Management → Version 1 posted Editorial decision: Minor revisions 09 May, 2025 Reviewers agreed at journal 25 Apr, 2024 Reviewers invited by journal 24 Apr, 2024 Editor assigned by journal 24 Apr, 2024 First submitted to journal 23 Apr, 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. 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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-4316281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295047527,"identity":"5b8bdbd9-bcd8-4883-9af8-63c9adccb32e","order_by":0,"name":"Heechan Han","email":"","orcid":"","institution":"Chosun University","correspondingAuthor":false,"prefix":"","firstName":"Heechan","middleName":"","lastName":"Han","suffix":""},{"id":295047528,"identity":"f1f07767-5cbc-46ca-9b91-e8b7c23f279c","order_by":1,"name":"Heeseung Park","email":"","orcid":"","institution":"Chosun University","correspondingAuthor":false,"prefix":"","firstName":"Heeseung","middleName":"","lastName":"Park","suffix":""},{"id":295047529,"identity":"58df4ece-7c11-498d-ab3c-df4520248f4a","order_by":2,"name":"donghyun kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYJACyQYGBsY29gaStCQAtfAcIFVLg0QCkcoNjrc/vDnzxx3ZPsm3Bz/zVNxj4G/vxq/Z4MwZY8sNCc+M26TzkqV5zhQzSJw5uwG/lhs5bJIPEg4ntknnGEjObEtgMJDIJaDl/vNnEC2SZ4x/zvxHjJYbDGaSG0BaJHjMJD42EKFF8kyOseWMNKBfeHLMLD4cS+Ah6Be+48cf3uyxuSM7v/2M8Y2EmgQ5/vZe/FoUDoCpA3ABHrzKQUC+AU3LKBgFo2AUjAIMAAD7ck6uDs36ZwAAAABJRU5ErkJggg==","orcid":"","institution":"Inha University","correspondingAuthor":true,"prefix":"","firstName":"donghyun","middleName":"","lastName":"kim","suffix":""}],"badges":[],"createdAt":"2024-04-24 07:35:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4316281/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4316281/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11269-025-04286-4","type":"published","date":"2025-10-15T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93956137,"identity":"b5d8305f-6d71-4b3a-be58-ab1ad117653b","added_by":"auto","created_at":"2025-10-20 16:11:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1308280,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4316281/v1_covered_cf886fa5-155e-42dc-897d-f055c2b666db.pdf"}],"financialInterests":"","formattedTitle":"Baseflow Separation for Improving Dam Inflow Prediction using Data-Driven Models","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":"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":"Baseflow separation, Dam inflow, Deep neural network, Random forest","lastPublishedDoi":"10.21203/rs.3.rs-4316281/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4316281/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImproving the accuracy of rainfall-runoff simulations is an important challenge for efficient water resource management. 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