Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models

The logistic regression (LR) and decision tree (DT) models are widely used for prediction analysis in a variety of applications. In the case of landslide susceptibility, prediction analysis is important to predict the areas which have high potential for landslide occurrence in the future. Therefore, the purpose of this study is to analyze and compare landslide susceptibility using LR and DT models by running three algorithms (CHAID, exhaustive CHAID, and QUEST). Landslide inventory maps (762 landslides) were compiled by reference to historical reports and aerial photographs. All landslides were randomly separated into two data sets: 50% were used to establish the models (training data sets) and the rest for validation (validation data sets). 20 factors were considered as conditioning factors related to landslide and divided into five categories (topography, hydrology, soil, geology, and forest). Associations between landslide occurrence and the conditioning factors were analyzed, and landslide-susceptibility maps were drawn using the LR and DT models. The maps were validated using the area under the curve (AUC) method. The DT model running the exhaustive CHAID algorithm (prediction accuracy 90.6%) was better than the DT CHAID (AUC = 90.2%), LR (AUC = 90.1%), and DT QUEST (84.3%) models. The DT model running the exhaustive CHAID algorithm is the best model in this study. Therefore, all models can be used to spatially predict landslide hazards.