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This paper proposes a statistical model for mapping global\udlandslide susceptibility based on logistic regression. After investigating\udexplanatory factors for landslides in the existing literature, five factors\udwere selected for model landslide susceptibility: relative relief, extreme\udprecipitation, lithology, ground motion and soil moisture. When building the\udmodel, 70 % of landslide and nonlandslide points were randomly selected\udfor logistic regression, and the others were used for model validation. To\udevaluate the accuracy of predictive models, this paper adopts several\udcriteria including a receiver operating characteristic (ROC) curve method.\udLogistic regression experiments found all five factors to be significant in\udexplaining landslide occurrence on a global scale. During the modeling process,\udpercentage correct in confusion matrix of landslide classification was\udapproximately 80 % and the area under the curve (AUC) was nearly 0.87.\udDuring the validation process, the above statistics were about 81 % and\ud0.88, respectively. Such a result indicates that the model has strong\udrobustness and stable performance. This model found that at a global scale,\udsoil moisture can be dominant in the occurrence of landslides and topographic\udfactor may be secondary

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