GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Artificial Neural Networks

GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Artificial Neural Networks | 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 GIS-Based Landslide Susceptibility Mapping Using Frequency Ratio and Artificial Neural Networks Sudip KC, Sandesh KC, Sauraj Gurung, Ujwal Paudel, Vijay Ale This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7302600/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 On Nepal’s steep hillsides, preventing landslides is an extremely challenging task. Every year, the country is affected by landslides, causing a significant number of deaths, loss of life and property, displacing families, and blocking the roads. Landslide susceptibility mapping is vital to safeguard life and property. This study focuses on preparing landslide susceptibility maps of two locations in Nepal using Frequency Ratio (FR) and Artificial Neural Network (ANN). A total of 329 landslides were mapped using historical landslide data, satellite images, and field surveys to generate the Landslide inventory maps. Landslide susceptibility mapping was performed based on twelve causative factors, namely slope gradient, slope aspect, curvature, distance to road, distance to stream, distance to fault, land cover and land use, geology, parent soil, average annual precipitation, Stream Power Index (SPI) and Topographic Wetness Index (TWI). The Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) analysis was performed to evaluate the accuracy of the models. The calculated AUC values for FR and ANN were found to be 85.62% and 87.7%, respectively, indicating that the landslide susceptibility prediction is fairly accurate. The landslide susceptibility maps produced using both methods were classified into five classes: very low, low, moderate, high, and very high. The final landslide susceptibility maps can be used for disaster risk reduction, land use planning, and early warning systems. Landslide Susceptibility Mapping Frequency Ratio Artificial Neural Network GIS AUC Values Full Text Additional Declarations No competing interests reported. Supplementary Files FieldData.pdf code.pdf 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-7302600","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527732610,"identity":"a9eae05a-d9fc-41c9-9b73-3fc645c695fd","order_by":0,"name":"Sudip 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Every year, the country is affected by landslides, causing a significant number of deaths, loss of life and property, displacing families, and blocking the roads. Landslide susceptibility mapping is vital to safeguard life and property. This study focuses on preparing landslide susceptibility maps of two locations in Nepal using Frequency Ratio (FR) and Artificial Neural Network (ANN). A total of 329 landslides were mapped using historical landslide data, satellite images, and field surveys to generate the Landslide inventory maps. Landslide susceptibility mapping was performed based on twelve causative factors, namely slope gradient, slope aspect, curvature, distance to road, distance to stream, distance to fault, land cover and land use, geology, parent soil, average annual precipitation, Stream Power Index (SPI) and Topographic Wetness Index (TWI). The Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) analysis was performed to evaluate the accuracy of the models. The calculated AUC values for FR and ANN were found to be 85.62% and 87.7%, respectively, indicating that the landslide susceptibility prediction is fairly accurate. The landslide susceptibility maps produced using both methods were classified into five classes: very low, low, moderate, high, and very high. 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