Abstract
ABSTRACTThe 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.
Highlights
Landslides are natural disasters that produce intensive, widespread damage to buildings and infrastructures, and they are the cause of countless casualties and economic distress in many nations worldwide (Akgun and Tu€rk 2010)
The results demonstrate that the RF-na€ıve Bayes tree (NBT) model showed the highest accuracy (93.1%), followed by the functional tree (FT) (83.6%), NBT (83.3%), reduced-error pruning tree (REPTree) (83.1), and logistic model tree (LMT) (82.6%) models
A novel hybrid artificial intelligence approach based on the rotation forest ensembles (RFEs) and NBT classifiers was proposed to assess the landslide susceptibilities in Langao County, China
Summary
Landslides are natural disasters that produce intensive, widespread damage to buildings and infrastructures, and they are the cause of countless casualties and economic distress in many nations worldwide (Akgun and Tu€rk 2010). These models were employed by some researchers for the production of geomorphological landform susceptibility maps to examine karst collapse features (Papadopoulou-Vrynioti et al 2013) and conduct multi-hazard assessments in urban areas (Bathrellos et al 2012; Chousianitis et al 2016; Bathrellos et al 2017)
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