Landslide Susceptibility Mapping on Global Scale using Method of Logistic Regression

This paper proposes a quantitative model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literatures, five factors were selected to model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building model, 70% of landslide and non-landslide points were randomly selected for logistic regression, and the others were used for model validation. For evaluating the accuracy of predictive models, this paper adopts several criteria including receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80% and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81% and 0.88, respectively. Such result indicates that the model has strong robustness and stable performance. Existing studies of global landslide susceptibility mapping have generally used qualitative methods based on expert knowledge. The accumulation of global landslide data makes it practical to mapping global landslide susceptibility quantitatively. This quantitative assessment found that at a global scale, soil moisture dominates the occurrence of landslides and topographic factor is secondary.