Combined landslide displacement prediction model based on multi-strategy fusion and improved optimization

Combined landslide displacement prediction model based on multi-strategy fusion and improved optimization | 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 Combined landslide displacement prediction model based on multi-strategy fusion and improved optimization Yuanfa Ji, Zijun Lin, Xiyan Sun, Jin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6963877/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 This study presents a combined prediction model grounded in multi-strategy integrated optimization to tackle the issues of high complexity, non-linearity, and inadequate prediction accuracy in landslide displacement. An improved SFOA incorporating Lévy flight, dynamic exploration adjustment, and stagnation detection enhances global search and convergence. OSFOA is then applied to optimize CEEMDAN, using minimum envelope entropy as the evaluation criterion to reduce hyperparameter subjectivity and decompose cumulative displacement into multi-scale components. The trend term is predicted using a Bayesian-optimized ARIMA model. Periodic and stochastic components are further decomposed by VMD to extract complex frequency features, with Bayesian-optimized SVR models constructed for their prediction. To enhance performance, GRA-MIC is employed to select key influencing factors and optimize input variables for periodic and stochastic terms. This investigation demonstrates that the proposed approach reduces RMSE by 82% and 52% relative to conventional optimization and single decomposition models, respectively. Comparative experiments involving five representative prediction models confirm its superior accuracy and stability over existing methodologies. Additionally, this study introduces an efficient novel framework for complex landslide displacement prediction and early-warning applications. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Landslide displacement prediction Improved SFOA algorithm CEEMDAN decomposition VMD secondary decomposition Bayesian optimization SVR prediction model 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-6963877","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484390642,"identity":"b73bc80e-51a4-4fc0-8320-c438f5fe2fd1","order_by":0,"name":"Yuanfa Ji","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuanfa","middleName":"","lastName":"Ji","suffix":""},{"id":484390643,"identity":"f63fe0f3-78b8-421b-b6d5-77e49c2eedb2","order_by":1,"name":"Zijun Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPmYgkcDDwMDG3nz8wwcDGzuCWthgWvh5jqUxzihISyasBcaQnOFjxszz4RBjA0Et7LyHPzyQOZxncIPB7LGNwQFmBvbDRzfgdxhfgkECz+Fig9sN6cY5Bnf4GHjS0m7g18JjkADUkrjhzoED0jkGz5gZJHjMCGo5ANZyI7FB2sLgMGMDEVoMG0BaZs5IZpNmIFKLMTCQ0xP7eY4xG/YYpCWzEfILP/8Z448/e6wT29j7Pz748cfGjp/98DG8WsCAsQfZXoLKweAHccpGwSgYBaNghAIAHTBHKLyNEm0AAAAASUVORK5CYII=","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":true,"prefix":"","firstName":"Zijun","middleName":"","lastName":"Lin","suffix":""},{"id":484390644,"identity":"454fe69a-6149-49a8-822b-2a5a206bd1bf","order_by":2,"name":"Xiyan Sun","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiyan","middleName":"","lastName":"Sun","suffix":""},{"id":484390647,"identity":"88103603-c348-423d-a302-f95eae45b99a","order_by":3,"name":"Jin Wang","email":"","orcid":"","institution":"Guangxi Industry and Research Low-Altitude Economy Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-24 09:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6963877/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6963877/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88658030,"identity":"5cfcd260-8ad4-4337-9ef3-2c1bd2667aae","added_by":"auto","created_at":"2025-08-08 20:01:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2132149,"visible":true,"origin":"","legend":"","description":"","filename":"Combinedlandslidedisplacementpredictionmodelbasedonmultistrategyfusionandimprovedoptimization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6963877/v1_covered_69947827-1460-4e30-bb12-aeb475f5eb15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combined landslide displacement prediction model based on multi-strategy fusion and improved optimization","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":"Landslide displacement prediction, Improved SFOA algorithm, CEEMDAN decomposition, VMD secondary decomposition, Bayesian optimization, SVR prediction model","lastPublishedDoi":"10.21203/rs.3.rs-6963877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6963877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a combined prediction model grounded in multi-strategy integrated optimization to tackle the issues of high complexity, non-linearity, and inadequate prediction accuracy in landslide displacement. An improved SFOA incorporating L\u0026eacute;vy flight, dynamic exploration adjustment, and stagnation detection enhances global search and convergence. OSFOA is then applied to optimize CEEMDAN, using minimum envelope entropy as the evaluation criterion to reduce hyperparameter subjectivity and decompose cumulative displacement into multi-scale components. The trend term is predicted using a Bayesian-optimized ARIMA model. Periodic and stochastic components are further decomposed by VMD to extract complex frequency features, with Bayesian-optimized SVR models constructed for their prediction. To enhance performance, GRA-MIC is employed to select key influencing factors and optimize input variables for periodic and stochastic terms. This investigation demonstrates that the proposed approach reduces RMSE by 82% and 52% relative to conventional optimization and single decomposition models, respectively. Comparative experiments involving five representative prediction models confirm its superior accuracy and stability over existing methodologies. 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