Application of an ensemble learning model based on random subspace and a J48 decision tree for landslide susceptibility mapping: a case study for Qingchuan, Sichuan, China

Landslides are a serious natural hazard in the world. A map of landslide susceptibility can help to effectively reduce losses. In this paper, a hybrid ensemble technique based on random subspace (RS) and a J48 decision tree named RS–J48T was proposed for landslide susceptibility mapping. This model could enhance the effect of a single classifier significantly and solve the problem of overfitting. Qingchuan County, Sichuan (China) was taken as a study area. A geospatial database which consisted of 640 landslide locations and 12 factors was constructed for this study. The J48 decision tree, artificial neural network (ANN), and other ensemble techniques like AdaBoost and Bagging, were selected for comparison. Receiver operating curves and some statistical indices were used for model validation. The results showed that the RS–J48T model had the better fitting capability (AUC = 0.875), and the best prediction capability (AUC = 0.769) compared to other models. Overall, the novel hybrid model could be a promising way for generating landslide susceptibility maps for other prone areas.