A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China

Landslide susceptibility maps are vital for planning development activities in the mountainous areas in China. The main goal of this study was to produce landslide susceptibility mapping by statistical index (SI) and certainty factor (CF) models for the Shangzhou District of Shangluo City, China. For this purpose, a landslide inventory map with a total of 145 landslide locations was compiled from various sources such as aerial photographs and field surveys, out of which 101 (70 %) were randomly selected for training the models, while the remaining 44 (30 %) were used for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope angle, slope aspect, curvature, elevation, lithology, distance to faults, distance to rivers, distance to roads, precipitation, and peak ground acceleration were considered in this study. The validation of landslide susceptibility maps were carried out using areas under the curve (AUC). From the analysis, it is seen that the CF model with a training accuracy of 70.48 % and predictive accuracy of 68.86 % performs slightly better than SI model (training accuracy, 70.19 %; predictive accuracy, 68.67 %). Overall, both of these two models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning for the study area and other similar areas in the world.