A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting

A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting | 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 A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting Bo Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4803200/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The Great Lakes are vital freshwater resource for both the United States and Canada. Therefore, the importance of this research lies in its potential to provide timely and accurate information for decision-makers. Improved water level predictions can aid in flood risk management, optimize water resource allocation, and support ecological conservation efforts. This study was conducted to completely eliminate traditional machine learning models’ lag effects with phase space reconstruction (PSR). The prediction was conducted using historical monthly mean water level datasets of Lake Ontario for the period 1918–2023, divided into training (1918–2002) and testing (2003–2023) datasets. The results revealed that PSR- RF outperform the standard random forest, KNN and LSTM models across all metrics, including Correlation Coefficient (0.999), Nash–Sutcliffe Efficiency (0.998), Root Mean Squared Error (0.014), Coefficient of Determination (0.998), and the slope and intercept of the regression equation (𝑦 = 0.98𝑥+1.484). Lake water prediction Phase space reconstruction Random Forest K-Nearest Neighbors (KNN) Hybrid predictive model Lake Ontario Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 02 Mar, 2026 Reviewers agreed at journal 28 Jul, 2024 Reviewers invited by journal 27 Jul, 2024 Editor assigned by journal 26 Jul, 2024 First submitted to journal 25 Jul, 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. 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-4803200","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":332550062,"identity":"33d4a4d0-5d0b-4866-9db7-74a6e8fd52e2","order_by":0,"name":"Bo Yuan","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4894-7733","institution":"Shanghai Normal University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2024-07-25 16:12:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4803200/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4803200/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63059323,"identity":"d0ce758f-3808-4c8e-b8b5-72955bd0c2dd","added_by":"auto","created_at":"2024-08-22 15:52:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2998316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4803200/v1_covered_633d2e1c-93d6-4f91-90fc-b8e96d115f51.pdf"}],"financialInterests":"","formattedTitle":"A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting","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":"Lake water prediction, Phase space reconstruction, Random Forest, K-Nearest Neighbors (KNN), Hybrid predictive model, Lake Ontario","lastPublishedDoi":"10.21203/rs.3.rs-4803200/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4803200/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Great Lakes are vital freshwater resource for both the United States and Canada. Therefore, the importance of this research lies in its potential to provide timely and accurate information for decision-makers. Improved water level predictions can aid in flood risk management, optimize water resource allocation, and support ecological conservation efforts. This study was conducted to completely eliminate traditional machine learning models\u0026rsquo; lag effects with phase space reconstruction (PSR). The prediction was conducted using historical monthly mean water level datasets of Lake Ontario for the period 1918\u0026ndash;2023, divided into training (1918\u0026ndash;2002) and testing (2003\u0026ndash;2023) datasets. The results revealed that PSR- RF outperform the standard random forest, KNN and LSTM models across all metrics, including Correlation Coefficient (0.999), Nash\u0026ndash;Sutcliffe Efficiency (0.998), Root Mean Squared Error (0.014), Coefficient of Determination (0.998), and the slope and intercept of the regression equation (\u0026#119910; = 0.98\u0026#119909;+1.484).\u003c/p\u003e","manuscriptTitle":"A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 15:44:37","doi":"10.21203/rs.3.rs-4803200/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revisions","date":"2026-03-02T10:54:58+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-07-29T02:00:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-27T14:14:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-26T05:32:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Water Resources Management","date":"2024-07-25T12:11:27+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"f513a28f-5712-45cc-bf60-fa2f31e9b8cf","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T06:01:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-22 15:44:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4803200","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4803200","identity":"rs-4803200","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}