Landslide susceptibility modelling using different advanced decision trees methods

Abstract

ABSTRACTIn this paper, decision trees machine learning algorithms, namely Random Forest (RF), Alternating Decision Tree (ADT), and Logistic Model Tree (LMT), were applied for modelling of susceptibility of landslides at the Luc Yen district, Northern Vietnam. These methods were evaluated to compare the performance of models and for selection of the best model for landslide susceptibility mapping and prediction. In this study, data of 95 landslides events were analysed with 10 landslide affecting factors using the Correlation-Based Feature Selection (CFS). These factors are land use, elevation, slope, distance to roads, aspect, curvature, distance to faults, rainfall, lithology, and distance to rivers. Receiver Operating Characteristic (ROC) curve, statistical indices (sensitivity, specificity, and kappa), and Chi-square test were utilised for validating and comparing the models performance. The modelling results show that the performance of RF model (AUC = 0.839) is the best with the data at hand compared to the ADT model (0.827) and the LMT (0.809) model. The RF should be applied for the better landslide susceptibility mapping and management.