Research of the Multi-input EMD-Bi-LSTM for reservoir water level prediction

Research of the Multi-input EMD-Bi-LSTM for reservoir water level 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 Article Research of the Multi-input EMD-Bi-LSTM for reservoir water level prediction Hailiang Tang, Hyunho Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6507933/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The water level of reservoirs plays a crucial role in the local ecological environment, influencing various aspects of surrounding ecosystems. Given the numerous factors that control reservoir water levels, this paper proposes an advanced reservoir water level prediction model, which utilizes a hybrid algorithm combining Multi-Input Empirical Mode Decomposition (EMD) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM). By incorporating hydrometeorological data and reservoir operation conditions as input variables, the Multi-Input EMD-Bi-LSTM model effectively captures and addresses the inherent nonlinear and nonstationary characteristics of time series data, thereby improving the accuracy and stability of reservoir water level predictions. The proposed Multi-Input EMD-Bi-LSTM model is compared with other models, including EMD-RNN、EMD-LSTM and EMD-GRU. The results demonstrate that the Multi-Input EMD-Bi-LSTM model significantly outperforms these traditional models regarding prediction accuracy and reliability. This advantage is attributed to the model's ability to handle complex multi-scale temporal patterns in the data, which simpler models often overlook. The enhanced model supports water resource management and contributes to planning responses to extreme weather events, which are becoming increasingly frequent and severe due to climate change. Earth and environmental sciences/Hydrology Physical sciences/Mathematics and computing/Computer science Bi-LSTM EMD Reservoir Water Level Prediction Time Series Full Text Additional Declarations No competing interests reported. Supplementary Files AdditionalNotes.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 30 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 02 May, 2025 Editor assigned by journal 02 May, 2025 Editor invited by journal 25 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 22 Apr, 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. 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-6507933","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451978951,"identity":"ea3522f4-c693-415b-949f-83bcf75ab1fd","order_by":0,"name":"Hailiang Tang","email":"","orcid":"","institution":"Kunsan National University","correspondingAuthor":false,"prefix":"","firstName":"Hailiang","middleName":"","lastName":"Tang","suffix":""},{"id":451978952,"identity":"1ebe1624-8408-4f0f-986c-e22993fd5cc0","order_by":1,"name":"Hyunho Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACAwkwZWOAEOIhTksa6VoOk6DFXLrH8HPBr/PGBrfbH34uYLCTZ+A5+wCvFss5Z4ylZ/bdNjO4A2TMYEg2bOBtN8CrxeBGjoE0b89tGyCDQZqHgTmBgZ+NgF9u5Bj/5u05B9SS/vg3D0M9UVrMpHl+HDAzuJEAZDAcTmDgbSOkJa3Mmrch2VgSqNeax+C4YRvPMUJakjff5vljZ9gHdNhtnopqeX6eNPxaGBg4DBgY4U4BhhUBn4AA+wMGhj+ElY2CUTAKRsEIBgCuwD9lwDigvgAAAABJRU5ErkJggg==","orcid":"","institution":"Kunsan National University","correspondingAuthor":true,"prefix":"","firstName":"Hyunho","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-04-23 01:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6507933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6507933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82119201,"identity":"44755721-2baa-4408-9802-af4e6f54d2d4","added_by":"auto","created_at":"2025-05-07 03:08:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":562714,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsSubmissionResearchofthemultiinputEMDBiLSTMforreservoirwaterlevelprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6507933/v1_covered_0af7539d-6922-4888-af83-e56f567561eb.pdf"},{"id":82116366,"identity":"0df46133-b823-40c7-afd1-c81cee1381d2","added_by":"auto","created_at":"2025-05-07 02:44:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13760,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalNotes.docx","url":"https://assets-eu.researchsquare.com/files/rs-6507933/v1/80233e203c98c03b04d6a78d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research of the Multi-input EMD-Bi-LSTM for reservoir water level prediction","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bi-LSTM, EMD, Reservoir Water Level Prediction, Time Series","lastPublishedDoi":"10.21203/rs.3.rs-6507933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6507933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe water level of reservoirs plays a crucial role in the local ecological environment, influencing various aspects of surrounding ecosystems. 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