Landslide risk zoning in Ruijin, Jiangxi, China
Abstract. Landslides are one of the major geohazards threatening human society. This study was aimed at conducting such a hazard risk prediction and zoning based on an efficient machine learning approach, Random Forest (RF), for Ruijin, Jiangxi, China. Multiple geospatial and geo-environmental data such as land cover, NDVI, landform, rainfall, stratigraphic lithology, proximity to faults, to roads and to rivers, depth of the weathered crust, etc., were utilized in this research. After pre-processing, including digitization, linear feature buffering and value assignment, 19 hazard-causative factors were eventually produced and converted into raster to constitute a 19-band geo-environmental dataset. 155 observed landslides that had truly taken places in the past 10 years were utilized to establish a vector layer. 70 % of the disaster sites (points) were randomly selected to compose a training set (TS) and the remained 30 % to form a validation set (VS). A number of non-risk samples were identified in low slope (