Application of an Integrated Model Based on Bivariate and Multivariate Method in Landslide Susceptibility Mapping

Landslides usually result in human losses and economic damages in mountainous areas especially for Himalayan areas. Landslide susceptibility mapping (LSM) is a key approach for avoiding hazard and risk. This study aims to explore an improved model combining multivariate and bivariate statistical methods for LSM. Four models were established as logistic regression (LR), LR integrated with certain factor (CF), LR integrated with frequency ratio (FR) and LR integrated with information value method (IV) and their performance was compared in LSM. Firstly, a landslide inventory map with 313 determined landslide events was prepared and 12 predisposing factors were selected. Secondly, the dataset was randomly divided into two parts, 75% of which was used for modeling and 25% for validation. Finally, area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. Results show that the performance of IVLR model is the best (AUC 0.792 and accuracy=78.8%). Besides, the LSM constructed by IVLR model did a reasonable job at predicting the distribution of susceptible areas. It identified the major factors and intervals of high susceptibility that profile curvature greater than 0.1, less than 2 km from the stream, maximum elevation difference greater than 1200 m and rainfall between 440 and 450 mm were prone to landslide. The conclusion reveals that the quality of LSM can be improved by comparing and combining the bivariate and multivariate methods, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.