Physical guided machine learning landslide susceptibility assessment method based on QGIS secondary development

Physical guided machine learning landslide susceptibility assessment method based on QGIS secondary development | 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 Physical guided machine learning landslide susceptibility assessment method based on QGIS secondary development Yingfan Wang¹, Jian Huang¹, Pusheng Zhang¹, Xin Yao¹, Meisen He¹, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7298273/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 Landslide susceptibility assessment is challenging due to the complex interplay of environmental factors and the limitations of traditional machine learning models, which often lack interpretability and rely on uncertain negative sample selection. This study introduces a physics-informed data-driven approach that integrates physical slope stability models (SINMAP and TRIGRS) with machine learning via a QGIS-based framework to address these challenges. Results demonstrate that the physics-informed approach significantly enhances model performance, with AUC improvements of 5.54–12.01% over traditional random sampling. The TRIGRS-constrained Random Forest model achieved the best overall performance, with notable gains in accuracy (11.33%), precision (4.18%), recall (27.99%), F1-score (16.70%), and AUC (12.01%). SHAP analysis further elucidated the influence of key factors, showing that low NDVI values, lower elevations, and proximity to roads heighten landslide risk—insights crucial for targeted mitigation. This study’s physics-informed framework not only improves prediction accuracy but also enhances model interpretability, offering a scalable solution for landslide susceptibility assessment in typhoon-prone areas. The method provides a robust technical reference for geohazard risk management, supporting disaster preparedness and infrastructure planning in vulnerable regions. landslide machine learning model physical model negative sample QGIS 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. 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-7298273","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524360813,"identity":"08e2a5c9-a791-4f0d-8300-7b3ed01b3446","order_by":0,"name":"Yingfan Wang¹","email":"","orcid":"","institution":"Chengdu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yingfan","middleName":"","lastName":"Wang¹","suffix":""},{"id":524360814,"identity":"24a48559-9d2d-46fc-8fe3-f70e9bec91a2","order_by":1,"name":"Jian 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