A REVIEW OF STATISTICAL APPROACHES USED FOR LANDSLIDE SUSCEPTIBILITY ANALYSIS WITH THE HELP OF REMOTE SENSING AND GIS TECHNOLOGY

Abstract

Aim of the study The goal of the present study, which was undertaken as a review, was to provide a critical analysis of statistical methods, used for landslide susceptibility modelling and associated terrain zonation from 2010 to 2023. Material and methods To critically review pertinent literature, the authors systematically searched for and compiled a substantial database using 139 articles that have been peer-reviewed. We assigned 5 categories/sub-categories of data for each article in the literature database, including the authors who have worked in landslide susceptibility, the trends in journal publishing on the subject, the periodic pattern of articles published from 2010 to 2023 (March), major factors that are considered for landslide susceptibility analysis, and the models that are used for landslide susceptibility analysis. Then these data are represented in graphical visualisation form. Results and conclusions There has been an increase in the number of publications from 2019 onwards on the topic of landslide susceptibility. In 20.8% of the studies, the Frequency Ratio (FR) model and Logistic Regression (LR) model have been chosen as the most popular approach for determining landslide vulnerability. Other than the logistic regression and frequency ratio model, other models like Fuzzy, Weight of Evidence (WoE), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest (RF) are also popular for landslide susceptibility analysis.