Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree

Abstract

Landslides are a form of soil erosion threatening the sustainability of some areas of the world. There is, therefore, a need to investigate landslide rates and behaviour. In this research, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a meta classifier based on reduced error pruning tree (REPTree) as a base classifier called RF-REPTree, for landslide susceptibility mapping (LSM) in the Kalaleh watershed, Golestan Province, Iran. Some benchmark models, including the open-source Java decision tree (J48), naive Bayes tree (NBTree), and REPTree were used to compare the designed model. A total of 249 landslide locations were identified and mapped. The group was split into training (70%) and testing (30%) data for modelling and reliability analysis. Based on a literature review and multi-collinearity tests, 16 landslide conditioning factors (LCFs) were selected. Of the LCFs, the topographical position index (TPI) had the highest correlation with landslide occurrence. The LSM produced by RF-REPTree revealed that nearly 29% of the study areas have high to very high landslide susceptibility (LS). Statistical analysis of the model results included the receiver operating characteristic curve (ROC), the efficiency test, the true skill statistic (TSS), and the kappa index. ROC demonstrated that the AUC values of RF-REPTree, REPTree, J48, and NBTree models were 0.832, 0.700, 0.695, and 0.759 for succession rate curves and 0.794, 0.740, 0.788, and 0.728 for prediction rate curves, respectively. Therefore, all models were judged to be acceptably accurate for LSM. Among the LS models, the RF-REPTree model achieved the highest accuracy, followed by REPTree, J48, and NBTree. The results of LSM can be used to target the mitigation of landslide hazards and provide a foundation for sustainable environmental planning.