Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Abstract. The western region of Chongqing seriously suffers from landslides. This paper evaluate the landslide hazard using GIS (Geographic Information System) technology and information value model and selecting seven influential factors including slope, aspect, elevation, rain, river, road and geological structure. The results show that the southern region of the study area are the most hazardous region. From the evaluation results, 20.6% of the total area suffers from high landslide risk and 13.1% suffers from very high risk, which is of important practical and immediate significance to the landslide prevention and reduction in the area. The evaluation result is basically coincident with the reality. The study further demonstrates that the information value model combined with geographical information system can quickly and effectively evaluate the spatial distribution and risk of the landslide hazard.