GIS-Based Landslide Susceptibility Mapping Using Logistic Regression, Instability Index, and Support Vector Machine: Case Study of the Jingshan River, Taiwan

Many techniques have been developed to produce landslide susceptibility maps. However, mapping the spatial distribution of landslide susceptibility involves critical steps. This study evaluated susceptibility to the occurrence of shallow landslides in upstream areas of the Jingshan River, Taiwan, where heavy rainfall has resulted in increased landslide occurrence and reservoir sedimentation. The landslide susceptibility case study was conducted using logistic regression, an instability index method, and support vector machine (SVM). A selection procedure was first developed to identify the factors influencing landslide occurrence. Historical landslide data were used to assess the corresponding parameters of each model. The receiver operating characteristic curve was then used to evaluate the accuracy of model results. The results indicated that the instability index method underestimated landslide susceptibilities in areas near the river. The instability index method was highly affected by the classifications of model factors. Of the models, the SVR model was the most accurate regarding landslide susceptibility in the study area. Employing this framework, governments may implement suitable, cost-effective improvements to structural measures and land-use planning.