Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China

Abstract

The traditional Stability INdex MAPping (SINMAP) model does not perform detailed divisions of study areas and neglects differences caused by the asymmetrical spatial distribution of geotechnical parameters; thus, the accuracy of the evaluation results is insufficient. In this study, the evaluation results of the SINMAP model were improved based on a combination with the certainty factor (CF) model, and the proposed method is referred to as the CF-SINMAP model. The Wuling Mountain area in Cili County of Hunan Province (China) was selected to verify the CF-SINMAP model. First, eight geological environmental factors in the region were analyzed by the CF method, including the slope, distance from fault, slope direction, distance from water, rock and soil type, elevation, distance from road and vegetation coverage. The rock and soil type, vegetation coverage and human engineering activities were determined as the key factors underlying landslide hazards. Then, the study area was divided into six regions based on the key factors, and the physical and mechanical parameters of each region were refined by the natural environment, formation lithology and human activities. Finally, the CF-SINMAP model was used to calculate and analyze the landslide hazard assessment results under different rainfall conditions. The results show that the CF-SINMAP model is more sensitive to rainfall compared with the traditional method and the unstable areas are mainly distributed along river valleys, reservoir banks and areas with continual human engineering activities. The area under the receiver operating characteristic (ROC) curve values was 0.75 and 0.61 for the CF-SINMAP and SINMAP models, respectively. Compared with the traditional SINMAP model, the CF-SINMAP model produces more reliable results. The rainfall threshold that induced the landslide disaster in Cili County, Hunan Province, was 90 mm/d. In summary, the CF-SINMAP model provides new ideas for the prediction of regional rainfall-induced landslides.