Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods

Abstract

This study evaluates and compares three machine learning models: K-Nearest Neighbour (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) for landslide susceptibility mapping for part of areas in Rangamati District, Bangladesh. The performance of these methods has been assessed by employing statistical methods such as the area under the curve (AUC) for success rate (SR) and prediction rate (PR), Kappa index, Qs index and Friedman's test. Results show that XGBoost had the best performance with the highest AUC for both SR (95.27%) and PR (90.63%), followed by RF (SR: 89.26%; PR: 84.74%) and KNN models (SR: 85.54%; PR: 81.02%). This study provides a useful analysis for the selection of the best model for landslide susceptibility mapping and that it will be helpful for disaster planning and risk reduction.

Keywords:

• Landslide susceptibility
• Rangamati
• K-nearest neighbour
• random forest
• XGBoost

Disclosure statement

The authors declared no conflict of interest.

Data availability statement

Data and codes are available at https://github.com/yrabby/XGBoost_Paper.