Dynamic evaluation of landslide susceptibility in a large-scale region based on time-series InSAR and multi-temporal cataloguing: a case study in Heifangtai, Gansu province

Dynamic evaluation of landslide susceptibility in a large-scale region based on time-series InSAR and multi-temporal cataloguing: a case study in Heifangtai, Gansu province | 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 Dynamic evaluation of landslide susceptibility in a large-scale region based on time-series InSAR and multi-temporal cataloguing: a case study in Heifangtai, Gansu province Qing Ling, Weizhi Li, jiebo qu, Qi Guo, Junguang Ren, Chenjian Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5755339/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 Landslide disasters are common in the mountainous regions of western China, making accurate classification of landslide hazard risk levels essential for effective geological disaster management. Data-driven models have achieved notable advancements in landslide susceptibility evaluation. However, they still encounter challenges such as a lack of dynamic feature data and an over-reliance on sample quality, which limits their effectiveness for large-scales. To address these issues, this paper introduces an integrated evaluation approach that combines time-series InSAR deformation data with data-driven models. This method initially incorporated time-series InSAR deformation monitoring data as dynamic factors, which were filtered through multivariate covariance analysis to construct a dynamic landslide susceptibility evaluation system. Additionally, only landslide samples were adopted to conduct large-scale landslide susceptibility analysis. Various machine learning algorithms, based on landslide and non-landslide samples, were also applied for model comparison. Building upon this foundation, the evaluation of landslide susceptibility was performed by integrating InSAR deformation data with multi-temporal cataloging. The Heifangtai region in Gansu Province was chosen as a case study. The results indicate that the maximum entropy (Maxent) model achieved the highest accuracy for large-scale susceptibility assessments. Incorporating time-series InSAR deformation into the dynamic landslide susceptibility model improved accuracy by about 0.36%, compared to models without this data. Additionally, the proportion of landslides identified in high and very high susceptibility zones increased by 4.29%. By using eight years of landslide catalog data as positive samples, the presented model achieved an accuracy of 99.26%, demonstrating that long-term, high-quality positive samples improve the precision and reliability of regional predictions. This study advances large-scale landslide risk assessment by integrating a dynamic evaluation system that accounts for data across multiple time periods. Heifangtai Susceptibility Evaluation Maxent Model Surface Deformation Multi-temporal slide cataloguing Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Jan, 2025 Reviewers invited by journal 22 Jan, 2025 Editor invited by journal 10 Jan, 2025 Editor assigned by journal 03 Jan, 2025 First submitted to journal 02 Jan, 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-5755339","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405612437,"identity":"27c34247-08b5-429f-9678-f9e12e4b1c34","order_by":0,"name":"Qing Ling","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Ling","suffix":""},{"id":405612438,"identity":"e73f3053-c407-4906-ba12-3d4ab9b90828","order_by":1,"name":"Weizhi Li","email":"","orcid":"","institution":"Lanzhou University of 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