A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

Abstract

The main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naïve Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted of eighteen conditioning factors that were selected using the information gain ratio (IGR) method. The model was evaluated using quantitative statistical criteria, including the sensitivity, specificity, accuracy, root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUC). Furthermore, the new model was compared with the NBT, functional tree (FT), logistic model tree (LMT) and reduced-error pruning tree (REPTree) soft computing benchmark models. The findings indicated that the RF-NBT model showed an increased prediction accuracy relative to the NBT model using both the training and validation datasets, and the RF-NBT model exhibited a greater capability for landslide susceptibility mapping. The new RF-NBT model also showed the most preferable results compared with the FT, LMT and REPTree models. Finally, an analysis of the landslide density (LD) using the RF-NBT model demonstrated that the very high susceptibility (VHS) class had the highest LD (3.552) among the landslide susceptibility maps. These results can be used for the planning and management of areas vulnerable to landslides in order to prevent damages caused by such natural disasters.