Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China)

Abstract

The purpose of this study is to evaluate and compare the results applying the statistical index and the index of entropy methods for estimating landslide susceptibility in Gongliu County, China. In order to do this, first, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs as well as by carrying out field surveys. Then the landslide inventory was randomly divided into two datasets 70 % (163 landslides) for training the models and the remaining 30 % (70 landslides) was used for validation purpose. The landslide conditioning factors consist of slope angle, slope aspect, altitude, general curvature, plan curvature, profile curvature, distance to rivers, distance to roads, normalized difference vegetation index, sediment transport index, rainfall, and lithology. The relationships between landslide distributions and these parameters were analyzed using the two models, and the results of both the models were then used to calculate the landslide susceptibility of the entire study area. Finally, the accuracy of the landslide susceptibility maps was evaluated based on the area under the curve (AUC) method. The validation results showed that the statistical index model (AUC = 82.51 %) is slightly lower than the index of entropy model (AUC = 82.80 %) for success rate. Nevertheless, for the prediction rate, it was found that the statistical index model (AUC = 77.90 %) is slightly lower than the index of entropy model (AUC = 77.41 %). The landslide susceptibility maps produced from this study were successful and can be useful for preliminary general land use planning and hazard mitigation purpose.