A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment

Landslide susceptibility assessment was performed using the novel hybrid model Bagging-based Naïve Bayes Trees (BAGNBT) at Mu Cang Chai district, located in northern Viet Nam. The model was validated using the Chi-square test, statistical indexes, and area under the receiver operating characteristic curve (AUC). In addition, other models, namely the Rotation Forest-based Naïve Bayes Trees (RFNBT), single Naïve Bayes Trees (NBT), and Support Vector Machines (SVM), were selected for the comparison. Results show that the novel hybrid model (AUC = 0.834) outperformed the RFNBT (0.830), SVM (0.805), and NBT (0.800). This indicates that the BAGNBT is a promising and better alternative method for landslide susceptibility modeling and mapping.