ABSTRACT
The 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%.
Acknowledgments
The authors are thankful to FAO, the Climate Change Knowledge portal, and Bhuvan and Bhukosh for providing the necessary datasets. The authors would also like to thank ESA for providing the Sentinel datasets. The authors also sincerely thank the anonymous reviewers and members of the editorial team for their comments.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Author contribution
All authors made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data. All authors have read and approved the final manuscript.
Data availability statement
Data sharing is not applicable to this article.