Displacement Prediction of Slow-Moving Landslides Using InSAR and Ensemble Regression Models based on Slope Units

Displacement Prediction of Slow-Moving Landslides Using InSAR and Ensemble Regression Models based on Slope Units | 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 Displacement Prediction of Slow-Moving Landslides Using InSAR and Ensemble Regression Models based on Slope Units Sandra Lucía Cobos, Victor Rodriguez-Galiano, Luigi Lombardo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7199518/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Ground displacement is a key indicator of slope instability, crucial for mitigating landslides amid climate-driven triggers. Interferometric Synthetic Aperture Radar (InSAR) has become a key tool for detecting and characterizing large-scale, slow-moving displacements. This study aims to (i) characterize ground deformation in an Andean region with known landslide activity using the Small Baseline Subset (SBAS) InSAR technique, and (ii) propose a novel predictive framework for slow-moving displacements. Line-of-Sight (LOS) displacement time series (TS) from 2021–2023 were aggregated based on mean and extreme values at the slope unit (SU) level and described using static and dynamic variables, with the latter computed over 7-28-day intervals. The decomposed TS (trend and periodic terms) were modeled using Extreme Gradient Boosting (XGBoost). The characterization of the study area identified three zones with slow-moving deformation, with LOS velocities ranging from − 68 to 388.6 mm/year (ascending) and − 245.7 to 165.1 mm/year (descending). The predictive framework showed best performance in Zone 1, where MaxAbsDtsdesc predicted the trend term with RMSE = 3.76 mm, R² = 1.00, MAPE = 3%. The poorest performance occurred in Zone 3, with periodic errors reaching up to 262.90 mm. Elevation, fault proximity, and groundwater storage (GWS) were key predictors for the trend term, while GWS dominated in the periodic term. Overall, mean-based TS outperformed maximum-based ones for the periodic term, while no consistent advantage was found between TS types for the trend term or between ascending and descending geometries. This approach offers valuable insights for territorial planning and risk management in landslide-prone Andean regions. Kinematic Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) Andean region Multitemporal and multivariate analysis Full Text Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Natural Hazards → Version 1 posted Editorial decision: Major revisions 20 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 25 Jul, 2025 First submitted to journal 24 Jul, 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-7199518","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493344283,"identity":"c679da28-a7d4-4b14-9db1-a47de43c92da","order_by":0,"name":"Sandra Lucía Cobos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACCSDmAbNAZAUDgwGJWs6QrIWxjQgt8rObnz14u+cwA39778GHP+cdljdnbz7A8KNiG04tjHOOmRvOeXaYQeLMuWRj3m2HDXf2HEtg7DlzG6cWZokEM2meA4cZDCRyzKQZtx1m3HAjx4CZsQ23FjaJ9G9wLZI/5xy2J6iFB2Q4TIsEb8PhRIJaJCRyyiTnHEiH+IXnWHryhjPHEg7i84v8jPRtEm8OWENC7EeNte2G480HH/yowK0FCprrG6AMMHmAkHogqMNgjIJRMApGwSiAAwD92lfZxy7o4AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1091-0000","institution":"Universidad Catolica de Cuenca","correspondingAuthor":true,"prefix":"","firstName":"Sandra","middleName":"Lucía","lastName":"Cobos","suffix":""},{"id":493344284,"identity":"74c6c65e-c80c-41ae-a62f-fb181b4ab38e","order_by":1,"name":"Victor Rodriguez-Galiano","email":"","orcid":"","institution":"Universidad de Sevilla Facultad de Geografia e Historia","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Rodriguez-Galiano","suffix":""},{"id":493344285,"identity":"efdd2e8e-e509-4867-bcef-65409309dd2d","order_by":2,"name":"Luigi Lombardo","email":"","orcid":"","institution":"University of Twente Faculty of Geo-Information Science and Earth Observation: Universiteit Twente Faculteit Geo-Informatie Wetenschappen en Aardobservatie","correspondingAuthor":false,"prefix":"","firstName":"Luigi","middleName":"","lastName":"Lombardo","suffix":""}],"badges":[],"createdAt":"2025-07-23 20:09:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7199518/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7199518/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-025-07829-5","type":"published","date":"2026-02-17T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103251255,"identity":"aca73fa3-73ce-40ff-bcb1-fd6712e7961a","added_by":"auto","created_at":"2026-02-23 16:07:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1872506,"visible":true,"origin":"","legend":"","description":"","filename":"FinalManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7199518/v1_covered_d947ac59-9f3b-4f6d-90d9-6caa9c3c35c1.pdf"}],"financialInterests":"","formattedTitle":"Displacement Prediction of Slow-Moving Landslides Using InSAR and Ensemble Regression Models based on Slope Units","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":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Kinematic, Small Baseline Subset (SBAS), Interferometric Synthetic Aperture Radar (InSAR), Andean region, Multitemporal and multivariate analysis","lastPublishedDoi":"10.21203/rs.3.rs-7199518/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7199518/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGround displacement is a key indicator of slope instability, crucial for mitigating landslides amid climate-driven triggers. Interferometric Synthetic Aperture Radar (InSAR) has become a key tool for detecting and characterizing large-scale, slow-moving displacements. This study aims to (i) characterize ground deformation in an Andean region with known landslide activity using the Small Baseline Subset (SBAS) InSAR technique, and (ii) propose a novel predictive framework for slow-moving displacements. Line-of-Sight (LOS) displacement time series (TS) from 2021\u0026ndash;2023 were aggregated based on mean and extreme values at the slope unit (SU) level and described using static and dynamic variables, with the latter computed over 7-28-day intervals. The decomposed TS (trend and periodic terms) were modeled using Extreme Gradient Boosting (XGBoost). The characterization of the study area identified three zones with slow-moving deformation, with LOS velocities ranging from \u0026minus;\u0026thinsp;68 to 388.6 mm/year (ascending) and \u0026minus;\u0026thinsp;245.7 to 165.1 mm/year (descending). The predictive framework showed best performance in Zone 1, where MaxAbsDtsdesc predicted the trend term with RMSE\u0026thinsp;=\u0026thinsp;3.76 mm, R\u0026sup2; = 1.00, MAPE\u0026thinsp;=\u0026thinsp;3%. The poorest performance occurred in Zone 3, with periodic errors reaching up to 262.90 mm. Elevation, fault proximity, and groundwater storage (GWS) were key predictors for the trend term, while GWS dominated in the periodic term. Overall, mean-based TS outperformed maximum-based ones for the periodic term, while no consistent advantage was found between TS types for the trend term or between ascending and descending geometries. 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