This study uses the inventories for landslides that were established by the Central Geological Survey for landslide data. A logistic regression, the Instability index method and a Support vector machine (SVM) are used to establish landslide susceptibility models and to produce landslide susceptibility maps for the upstream areas of Jing-Shan River. Ten causative factors for landslides are selected similarly to previous studies. A selection procedure is then used to reduce the number of factors. The receiver operating characteristic curve is used to evaluate the accuracy of the model results. The Logistic regression and the Instability index method both show that the roughness of the terrain is a critical factor for the susceptibility value. The instability index method can lead to possible underestimation around the river and the number of factor classifications can impact the results. SVM establishes the model by classifying the landslide data. The landslide susceptibility values are not reliant on particular factors.Therefore, the results for the model prediction are not influenced by the weights of the factors. The landslide susceptibilities are classified into four groups: low, medium, medium-high and high. SVM and Logistic regression are superior to the Instability index method because they identify landslides in medium-high and high susceptibility areas.The analysis of the area under the curve (AUC) gives an AUC value of 0.825 using SVM, 0.721 using logistic regression and 0.718 using the instability index method. This demonstrates that SVM is the best method to assess landslide risk in the research areas.
Key Words : Logistic regression, instability index, support vector machine, landslide susceptibility.