Multi-scenario landslide probabilistic hazard analysis based on a single rainfall event: A case of the Zhuzhou-Guangzhou section of Beijing-Guangzhou railway in China

Multi-scenario landslide probabilistic hazard analysis based on a single rainfall event: A case of the Zhuzhou-Guangzhou section of Beijing-Guangzhou railway in China | 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 Multi-scenario landslide probabilistic hazard analysis based on a single rainfall event: A case of the Zhuzhou-Guangzhou section of Beijing-Guangzhou railway in China Zhiwen Xue, Chong Xu, Jiale Jin, Chenchen Xie, Qihao Sun, Juanling Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5819371/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 calculates the absolute probability of landslides under varying rainfall scenarios along the Beijing-Guangzhou Railway from Zhuzhou to Guangzhou, aiming to enhance railway transportation safety. Using a Bayesian sampling strategy, a Logistic Regression (LR) model was developed for landslide hazard assessment based on the geological conditions and rainfall data along the railway. The model demonstrated strong predictive performance with an AUC value of 0.86 for both training and testing sets, showing no overfitting. Results indicated that when rainfall is less than 150 mm, over 70% of the study area has an absolute landslide probability below 0.1%. However, with rainfall exceeding 150 mm, landslide hazards increase significantly, with a rapid rise in areas where the probability ranges from 0.1–1%. When rainfall reaches 500 mm, about 60% of the region exhibits a landslide probability exceeding 1%. Under real rainfall scenarios (e.g., cumulative rainfall during the 10 days before June 7, 2020), areas with probabilities greater than 1% are mainly concentrated in Fogang County, northeast of Guangzhou, and eastern Zhuzhou, aligning with heavy rainfall distributions. The relationship between rainfall and landslide occurrence is highly non-linear, with probabilities increasing exponentially as rainfall rises. These results provide an effective tool for landslide hazard assessment along the railway and offer valuable data support for disaster warning and prevention measures. Absolute probability landslide probabilistic hazard single rainfall event different rainfall scenarios Bayesian thinking logistic regression model Full Text 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. 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-5819371","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":425071871,"identity":"de1eeb26-0585-431e-95a6-a67c0a4d2cf1","order_by":0,"name":"Zhiwen Xue","email":"","orcid":"","institution":"National Institute of Natural Hazards","correspondingAuthor":false,"prefix":"","firstName":"Zhiwen","middleName":"","lastName":"Xue","suffix":""},{"id":425071872,"identity":"c0517c8c-3e15-4e24-9b84-0c314f07e4bc","order_by":1,"name":"Chong 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Using a Bayesian sampling strategy, a Logistic Regression (LR) model was developed for landslide hazard assessment based on the geological conditions and rainfall data along the railway. The model demonstrated strong predictive performance with an AUC value of 0.86 for both training and testing sets, showing no overfitting. Results indicated that when rainfall is less than 150 mm, over 70% of the study area has an absolute landslide probability below 0.1%. However, with rainfall exceeding 150 mm, landslide hazards increase significantly, with a rapid rise in areas where the probability ranges from 0.1\u0026ndash;1%. When rainfall reaches 500 mm, about 60% of the region exhibits a landslide probability exceeding 1%. Under real rainfall scenarios (e.g., cumulative rainfall during the 10 days before June 7, 2020), areas with probabilities greater than 1% are mainly concentrated in Fogang County, northeast of Guangzhou, and eastern Zhuzhou, aligning with heavy rainfall distributions. 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