Local-scale landslide susceptibility mapping using the B-GeoSVC model

Abstract

Local-scale landslide susceptibility mapping (LSM) provides detailed information for decision making and planning. Most published landslide susceptibility maps lack reliable information at the local scale due to the spatial heterogeneity being ignored. To enrich the local-scale information of LSM, multiple information fusion methods for the local spatial heterogeneity and regional trends of control factors are needed. However, no information fusion method has been proposed for LSM yet. In this paper, we developed a new integrated statistical method, named B-GeoSVC, under the hierarchical Bayesian framework for LSM. Specifically, this model applied the GeoDetector method to fit the regional trends of control factors and employed spatially varying coefficients (SVC) model to fit the local spatial heterogeneity of each control factor. Then, the regional trends and local spatial heterogeneity information were fused within the hierarchical Bayesian framework. The B-GeoSVC model was verified using data from the Duwen basin of China, which was in the central region affected by the MS 8.0 Wenchuan earthquake that occurred on May 12, 2008. Under a cross-validation experiment, the prediction accuracy rate of the B-GeoSVC model was 86.09%, and the area under the curve was 0.93, which suggested that the B-GeoSVC model was able to achieve relatively accurate local-scale LSM and provide richer local information than traditional regional scale LSM. More importantly, not only the B-GeoSVC model could be employed as a general solution to fuse both regional and local-scale information for landslide mapping, but also offer new insights into the broader earth science and spatial statistics.