A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS

Abstract

This study investigates the application of information value (InV) and logistic regression (LR) models for producing landslide susceptibility maps (LSMs) of the Zigui–Badong area near the Three Gorges Reservoir in China. This area is subject to anthropogenic influences because the reservoir’s water level cyclically fluctuates between 145 and 175 m. In addition, the area suffers from extreme rainfall events due to the local climate and has experienced significant and widespread landslide events in recent years. In this study, a landslide inventory map was initially constructed using field surveys, aerial photographs, and a literature search of historical landslide records. Eight causative factors, including lithology, bedding structure, slope, aspect, elevation, profile curvature, plane curvature, and fractional vegetation cover, were then considered in the generation of LSMs by using the InV and LR models. Finally, the prediction performances of these maps were assessed through receiver operating characteristics (ROC) that utilized both success-rate and prediction-rate curves. The validation results showed that the area under the ROC curve for the InV model was 0.859 for the success-rate curve and 0.865 for prediction-rate curve; these results indicate the InV model surpassed the LR model (0.742 for success-rate curve and 0.740 for prediction-rate curve). Overall, the two models provided nearly similar results. The results of this study show that landslide susceptibility mapping in the Zigui–Badong area is viable with both approaches.