Integrating Geo-Environmental Factors and Ensemble Machine Learning for Landslide Susceptibility Assessment in Kodaikanal and its Environs, India

Integrating Geo-Environmental Factors and Ensemble Machine Learning for Landslide Susceptibility Assessment in Kodaikanal and its Environs, India | 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 Integrating Geo-Environmental Factors and Ensemble Machine Learning for Landslide Susceptibility Assessment in Kodaikanal and its Environs, India Dhanabalan SP, Jayanta Das, Jegankumar R, Sreedarsh TM, Sindhuja V, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8113160/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 is significant for disaster mitigation and sustainable land-use planning in geologically unstable areas, like Kodaikanal, South India. This present study examined the predictive capabilities of five machine-learning algorithms, Random Forest (RF), Gradient Boosting (GB), k-Nearest Neighbors (k-NN), Extra Trees (ET), and stacking ensemble model, along with a Frequency Ratio (FR) method, to create a landslide susceptibility map. The 70% of landslide and non-landslide points were used for training, and the remaining 30% testing to evaluate the model accuracy. The final susceptibility map was divided the area into five categories: very low, low, moderate, high, and very high. Zones with very high (7%), high (8%) were mainly located on steep slopes. The model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and receiver operating characteristic (ROC) curves. Overall, the GB showed the lowest MSE, RMSE, and MAE values, indicating the highest prediction accuracy, and RF also performed strongly, ET had moderate performance, and k-NN had weaker prediction. The Stacking ensemble has higher predictive accuracy with an AUC-ROC of 0.96%. This integrated approach supports early warning system, and sustainable land management in landslide-prone areas of Kodaikanal. Landslide susceptibility Machine learning Stacking ensemble Error matrix Sustainable land management 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. 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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-8113160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593591562,"identity":"02d7b0ac-6095-415a-922c-864f8d131fdb","order_by":0,"name":"Dhanabalan 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