LAUSR

Bivariate statistical analysis of data-driven approaches is widely used for landslide susceptibility assessment, and the frequency ratio (FR) method is one of the most popular. However, the results of such assessments are dominated by the number of classes and bounds of landslide-related causative factors, and the optimal assessment is unknown. This paper optimizes the frequency ratio method as an example of bivariate statistical analysis for landslide susceptibility mapping based on a case study of the Caiyuan Basin, a region with frequent landslides, which is located in the southeast coastal mountainous area of China. A landslide inventory map containing a total of 1425 landslides (polygons) was produced, in which 70% of the landslides were selected for training purposes, and the remaining were used for validation purposes. All datasets were resampled to the same 5 m × 5 m/pixel resolution. The receiver operating characteristic (ROC) curves of the susceptibility maps were obtained based on different combinations of dominating parameters, and the maximum value of the areas under the ROC curves (AUCs) as well as the corresponding optimal parameter was identified with an automatic searching algorithm. The results showed that the landslide susceptibility maps obtained using optimal parameters displayed a significant increase in the prediction AUC compared with those values obtained using stochastic parameters. The results also showed that one parameter named bin width has a dominant influence on the optimum. In practice, this paper is expected to benefit the assessment of landslide susceptibility by providing an easy-to-use tool. The proposed automatic approach provides a way to optimize the frequency ratio method or other bivariate statistical methods, which can further facilitate comparisons and choices between different methods for landslide susceptibility assessment.