Articles | Volume 17, issue 8
Research article
25 Aug 2017
Research article |  | 25 Aug 2017

Landslide susceptibility mapping on a global scale using the method of logistic regression

Le Lin, Qigen Lin, and Ying Wang

Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a 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 a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

Short summary
To address the issue of what can influence the occurrence of landslides on a global scale and to what impact those factors can have in a relatively objective way, we proposed to produce a global landslide susceptibility map using the method of logistic regression. We find out that topology may not be the first controlling factor of landslides, and finer resolution of DEM may not significantly contribute to the improvement of landslide model when the location precision of landslides is limited.
Final-revised paper