Coupled Flow CEEMDAN-SSA-BiLSTM-based predictive model

Coupled Flow CEEMDAN-SSA-BiLSTM-based predictive model | 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 Coupled Flow CEEMDAN-SSA-BiLSTM-based predictive model Xianqi Zhang, Yupeng Zheng, Yang Yang, Yike Liu, Kaiwei Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4385984/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 Flow is one of the important hydrological elements to study the water ecology and water environment of rivers in nature. Predicting flow is crucial for gathering valuable research data to aid in flood prevention, mitigation efforts, and the sustainable harnessing and utilization of water resources in the basin. To enhance the accuracy of flow prediction, a novel approach has been proposed. This methodology integrates the Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN) with a Long and Short-Term Memory (LSTM) model, further refined through the application of the Sparrow Search Algorithm (SSA). The result is a powerful and innovative Combined Runoff Prediction Model, referred to as CEEMDAN-SSA-BiLSTM. This integrated model aims to provide more reliable predictions for both long and short-term runoff scenarios, contributing to more effective water resource management and environmental preservation in the basin. The daily flow trends from 2016 to 2022 were analyzed at four hydrological stations, namely Huayuankou, Jiahetan, Gaocun, and Lijin. The overall process is to use 80% daily flow data trained to predict 20% daily flow. Combined with the evaluation indexes used, the final series of results obtained are compared with the prediction results of several models, such as LSTM, BiLSTM, and CEEMDAN-BiLSTM, in multiple ways. The ultimate comparative outcomes demonstrate that the CEEMDAN-SSA-BiLSTM coupling exhibits a notable level of accuracy in forecasting daily flow. It has less error compared to several other models. daily flow prediction CEEMDAN BiLSTM decomposition-prediction-reconstruction lower Yellow River Full Text Additional Declarations No competing interests reported. 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. 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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-4385984","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309506452,"identity":"c56f772f-0559-46de-8b80-f130af75efd7","order_by":0,"name":"Xianqi Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xianqi","middleName":"","lastName":"Zhang","suffix":""},{"id":309506453,"identity":"eedbdf42-e462-4e92-bb28-0a90df9db6b7","order_by":1,"name":"Yupeng Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACNv7mww8SDGx47NubDxCnhU/iWJrBg4o0GQOeYwnEaZFjyFGQfHDmkI2BRI4BkQ5jOMNgkNh2gMdcIufjjTcMdnK6DYS0MPceeJDYdofHsuftZss5DMnGZgcI2nIuAWjLMx6G47nbpHkYDiRuI6wlx0Aise0wUHHOMxK0JJw5zGNwIoeNSC2gQE6oSOOR7DlmbDnHgAi/yPc3H374w8DGnp+9+eGNNxV2cgS1oAAJHiKjBlkLqTpGwSgYBaNgRAAA/jtFEn7QMXYAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yupeng","middleName":"","lastName":"Zheng","suffix":""},{"id":309506454,"identity":"b40dd180-339f-415c-87f8-382a20aa517f","order_by":2,"name":"Yang Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":309506455,"identity":"7226386f-ad3a-43df-a7df-1eaf60106d85","order_by":3,"name":"Yike Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yike","middleName":"","lastName":"Liu","suffix":""},{"id":309506456,"identity":"7b37f17b-3391-46ed-a5e8-df4dc836df6b","order_by":4,"name":"Kaiwei Yan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kaiwei","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2024-05-08 02:32:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4385984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4385984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57853060,"identity":"a6472c0e-196c-47e1-ba3e-c2f72da604ce","added_by":"auto","created_at":"2024-06-06 12:22:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1163559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4385984/v1_covered_50255849-b17a-43aa-92fa-1c1f6c729022.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coupled Flow CEEMDAN-SSA-BiLSTM-based predictive model","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":"daily flow prediction, CEEMDAN, BiLSTM, decomposition-prediction-reconstruction, lower Yellow River","lastPublishedDoi":"10.21203/rs.3.rs-4385984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4385984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlow is one of the important hydrological elements to study the water ecology and water environment of rivers in nature. Predicting flow is crucial for gathering valuable research data to aid in flood prevention, mitigation efforts, and the sustainable harnessing and utilization of water resources in the basin. To enhance the accuracy of flow prediction, a novel approach has been proposed. This methodology integrates the Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN) with a Long and Short-Term Memory (LSTM) model, further refined through the application of the Sparrow Search Algorithm (SSA). The result is a powerful and innovative Combined Runoff Prediction Model, referred to as CEEMDAN-SSA-BiLSTM. This integrated model aims to provide more reliable predictions for both long and short-term runoff scenarios, contributing to more effective water resource management and environmental preservation in the basin. The daily flow trends from 2016 to 2022 were analyzed at four hydrological stations, namely Huayuankou, Jiahetan, Gaocun, and Lijin. 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