Application of Naive Bayes, kernel logistic regression and alternation decision tree for landslide susceptibility mapping in Pengyang County, China

The purpose of this research is to apply and compare the performance of the three machine learning algorithms-Naive Bayes (NB), kernel logistic regression (KLR), and alternation decision tree (ADT) to come up with landslide susceptibility maps for Pengyang County, a landslide-prone area in Ningxia Hui Autonomous Region, China. In the first phase, we constructed a landslide inventory map consisting of 972 landslides and the same quantity of non-landslides based on digital elevation model analysis, survey data and satellite images, then combined the two databases and classified into training and validating subsets randomly at the ratio of 70:30. Secondly, 13 conditional factors were prepared, and feature selection was performed using average merit. Subsequently, we used the area under the receiver operating characteristic curve (AUC), root mean square error, mean squared error, and frequency ratio precision to test the validity and prediction ability of the models. This outcome demonstrated that three models are all predictive and can generate adequate results in the study scope, and the ADT model is entitled with the best performance, whose AUC values are 0.844 for the training dataset and 0.838 for the validation dataset. The next is KLR (0.811 for the training dataset, 0.814 for the validation dataset) and then NB (0.808 for the training dataset, 0.797 for the validation dataset) models. Meanwhile, the frequency ratio precision of ADT model is 0.971, which is higher than KLR (0.844) and NB (0.810). The suggested landslide susceptibility map and corresponding method enable researchers and local authorities in future decision-making for geological disaster prevention and mitigation.