Jiale Wang

TL;DR: In this article, the authors used three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility.

TL;DR: Wang et al. as discussed by the authors used three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China.

TL;DR: The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models.

TL;DR: The results show that the KLR model has the highest AUC values and the highest degree of goodness-of-fits for both the training and validation datasets, respectively, and the NBTree model hasThe benefit of selecting the optimal machine learning techniques in landslide susceptibility mapping.

TL;DR: In this article, the authors compared the performance of frequency ratio (FR), index of entropy (IOE), and support vector machines with four kernel functions for landslide susceptibility mapping at Long County, China.