Sensitivity evaluation and prediction of extreme rainfall-induced landslides in Xinning, Hunan China, using Gumbel extreme value theory and random forest model

Sensitivity evaluation and prediction of extreme rainfall-induced landslides in Xinning, Hunan China, using Gumbel extreme value theory and random forest 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 Sensitivity evaluation and prediction of extreme rainfall-induced landslides in Xinning, Hunan China, using Gumbel extreme value theory and random forest model Yan Yu, Zhengqing Wang, Yulin Chen, Tianhao Zhao, Dinghao Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7941633/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Landslide sensitivity research prioritizes current assessments over predictive risk modeling Although rainfall is a well-documented landslide trigger, predictive frameworks for rainfall-induced landslides remain underdeveloped due to limited case studies This study addresses these gaps in Xinning County, China through: ( 1 ) identifying rainfall as the key external driver among 10 factors via certainty factor modeling; ( 2 ) predicting maximum rainfall intensities for 5-, 10-, 30-, 50-, and 100-year return periods using CHIRPS data, Google Earth Engine, and Gumbel extreme value theory; ( 3 ) assessing spatiotemporal sensitivity with a Random Forest Model Key results show: persistent high/very high sensitivity (HVHS) clusters in western (SM, WT Towns) and northeastern (XT Town) regions; escalating sensitivity in GQ, MTQ, HLS, and YDS (transitioning from low/ moderate levels to high/ very high levels); and decadal-scale expansion of very high sensitivity (VHS) areas in GQ/MTQ/HLS Towns, with 10-year return period extents doubling other intervals Quantitatively, the proportion of VHS zones in the county ranged between 32% and 129%, with the lowest value recorded in 2023 and the peak observed under the 10-year return period; GQ Town is the only area where VHS zones account for over 40% of the total spatial coverage under a 10-year return period; WT and SM Towns collectively exceed 80% HVHS coverage in 2023 Spatially, HVHS zones in central/northern lowlands align with median maximum daily rainfall, contrasting with low/very low sensitivity (LVLS) zones in southeastern/southwestern highlands linked to higher values Proactive mitigation in GQ/MTQ/HLS Towns is urged before the 10-year return period. Landslide Gumbel extreme value theory Random forest Sensitivity Evaluation Prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 25 Oct, 2025 Submission checks completed at journal 25 Oct, 2025 First submitted to journal 24 Oct, 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. 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-7941633","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539313734,"identity":"b852ab69-da8b-4717-8f59-96d87ebebee4","order_by":0,"name":"Yan Yu","email":"","orcid":"","institution":"University of South China","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yu","suffix":""},{"id":539313735,"identity":"3f2be51a-d95e-4427-8d47-f4e708d69eb1","order_by":1,"name":"Zhengqing 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rainfall intensities for 5-, 10-, 30-, 50-, and 100-year return periods using CHIRPS data, Google Earth Engine, and Gumbel extreme value theory; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) assessing spatiotemporal sensitivity with a Random Forest Model Key results show: persistent high/very high sensitivity (HVHS) clusters in western (SM, WT Towns) and northeastern (XT Town) regions; escalating sensitivity in GQ, MTQ, HLS, and YDS (transitioning from low/ moderate levels to high/ very high levels); and decadal-scale expansion of very high sensitivity (VHS) areas in GQ/MTQ/HLS Towns, with 10-year return period extents doubling other intervals Quantitatively, the proportion of VHS zones in the county ranged between 32% and 129%, with the lowest value recorded in 2023 and the peak observed under the 10-year return period; GQ Town is the only area where VHS zones account for over 40% of the total spatial coverage under a 10-year return period; WT and SM Towns collectively exceed 80% HVHS coverage in 2023 Spatially, HVHS zones in central/northern lowlands align with median maximum daily rainfall, contrasting with low/very low sensitivity (LVLS) zones in southeastern/southwestern highlands linked to higher values Proactive mitigation in GQ/MTQ/HLS Towns is urged before the 10-year return period.\u003c/p\u003e","manuscriptTitle":"Sensitivity evaluation and prediction of extreme rainfall-induced landslides in Xinning, Hunan China, using Gumbel extreme value theory and random forest model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 13:50:15","doi":"10.21203/rs.3.rs-7941633/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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