Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models

• Main Authors: Hyun-Joo Oh, Prima Riza Kadavi, Chang-Wook Lee, Saro Lee
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Geomatics, Natural Hazards & Risk
• Subjects: Area under the curve; evidential belief function model; landslide susceptibility; logistic regression; support vector machine model
• Online Access: http://dx.doi.org/10.1080/19475705.2018.1481147

The main purpose of this study was to produce landslide susceptibility maps using evidential belief function (EBF), logistic regression (LR) and support vector machine (SVM) models and to compare their results for the region surrounding Yongin, South Korea. We compiled a landslide inventory map of 82 landslides based on reports and aerial photographs and confirmed these data through extensive field surveys. All landslides were randomly separated into two data sets of 41 landslide data points each; half were selected to establish the model, and the remaining half were used for validation. We divided 18 landslide conditioning factors into the following four categories: topography factors, hydrology factors, soil map and forest map; these were considered for landslide susceptibility mapping. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the EBF, LR and SVM models. The three models were then validated using the area under the curve (AUC) method. According to the validation results, the prediction accuracy of the LR model (AUC = 94.59%) was higher than those of the EBF model (AUC = 92.25%) and the SVM model (AUC = 81.78%); the LR model also had the highest training accuracy.