Kshetrimayum, A.Ramesh, H.Goyal, A.2026-02-042024Journal of Spatial Science, 2024, , , pp. -14498596https://doi.org/10.1080/14498596.2024.2368156https://idr.nitk.ac.in/handle/123456789/21398The movement of rock, soil, and other debris down a slope or incline is a geological phenomenon known as a landslide. To analyze the landslide susceptibility (LS) in Manipur, the study develops and compares six heterogeneous models, specifically the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Deep Learning (DL) models were considered. The study found that the DL is the most intriguing model, with a total accuracy of 97.2%, followed by the RF, KNN, SVM, AHP, and FR, with respective accuracy levels of 94.5%, 93.1%, 92.6%, 85.5%, and 76.9%. © 2024 Mapping Sciences Institute, Australia and Geospatial Council of Australia.Analytical Hierarchy Process (AHP)deep learningFrequency Ratio (FR)Landslide susceptibility mappingmachine learningmodels comparison