Landslide susceptibility mapping for western coastal districts of India using geospatial techniques and eXplainable Artificial Intelligence

Landslide susceptibility mapping for western coastal districts of India using geospatial techniques and eXplainable Artificial Intelligence | 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 Landslide susceptibility mapping for western coastal districts of India using geospatial techniques and eXplainable Artificial Intelligence Dikshita A Shetkar, Bappa Das, Sujeet Desai, Gopal Mahajan, Parveen Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5648946/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 May, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted 9 You are reading this latest preprint version Abstract Landslide susceptibility mapping (LSM) assists in identifying probable zones for future landslide occurrences within a given location by considering various landslide-triggering factors. Most significantly, this mapping contributes to regional planning and the landslide mitigation procedure and raises public awareness and education on landslides. In the current study, LSM was conducted for western coastal districts of India using fourteen landslide triggering factors. For locating landslide-susceptible areas and to identify the best preforming model, a comparison between frequency ratio (FR), logistic regression (LR), machine learning (ML) and artificial intelligence models was performed. ML models used in this study were random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB) and deep neural network (DNN). Most of the area was covered by very low class, i.e., 60.12% followed by low (13.50%), moderate (10.54%), high (8.04%) and very high (7.79%) classes, respectively. From the variable importance plots, it was found that factors such as slope, TRI, LS-factor, distance to road and rainfall were the most significant landslide-triggering factors. The area under the ROC curve (AUC) was utilised to validate the models. The results of the AUC revealed that the RF model showed an excellent accuracy rate of 0.993, followed by XGB (0.992), SVM (0.955), DNN (0.949), LR (0.919), and FR (0.906) model. The ranking based on multiple model evaluation parameters using validation dataset revealed DNN as the best-performing model. It was concluded that the performance of ML models was excellent compared to the FR model. The results of this study could help to identify landslide-vulnerable areas and adopt suitable preventive measures for mitigating the likely occurrence of future landslide events. Coastal ecosystem Deep Neural Network Landslides Logistic Regression Random Forest PDP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 May, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted Editorial decision: Revision requested 22 Mar, 2025 Reviews received at journal 26 Jan, 2025 Reviewers agreed at journal 26 Jan, 2025 Reviewers agreed at journal 20 Jan, 2025 Reviewers agreed at journal 20 Jan, 2025 Reviewers invited by journal 20 Jan, 2025 Editor assigned by journal 16 Dec, 2024 Submission checks completed at journal 16 Dec, 2024 First submitted to journal 15 Dec, 2024 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-5648946","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407320335,"identity":"e980a806-a226-4277-9700-c7f9fb14b98e","order_by":0,"name":"Dikshita A Shetkar","email":"","orcid":"","institution":"ICAR – Central Coastal Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Dikshita","middleName":"A","lastName":"Shetkar","suffix":""},{"id":407320336,"identity":"ae058d3f-814f-42d3-bfd8-62bd7d930e5d","order_by":1,"name":"Bappa 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