The earthquake-induced landslide susceptibility assessment is one of the important parts in the researches of secondary disasters of earthquake. In view of the large amount of data, the rich information, the complex relationship, it is a very difficult task. This paper takes Lushan in the 2013 Lushan earthquake as the research area. Massive landslides were triggered by this earthquake. Among these landslides, 226 landslides are interpreted based on aerial photographs in Lushan, which are verified by the field investigation. Then 9 impact factors are selected by the Pearson correlation analysis, including the elevation, the slope, the aspect, the curvature classification, the slope structure, the lithology, the distance from drainages, the distance from faults, and the peak ground acceleration. The relevant vector machine(RVM) is a new learning procedure based on the statistical learning theory, and a genetic algorithm(GA) is adopted to optimize the parameter of the RVM. The proposed GA-RVM model is used to calculate the landslide susceptibility value, to produce susceptibility zoning. The statistical data of the susceptibility zoning are as follows:(1)the accuracy rate of the landslides is 99.74%; (2)the density of the landslides in a high susceptibility zoning is 27.4057 per square kilometers. The result shows that the relevant vector machine model is better than the support vector machine and is suitable for the earthquake-induced landslide susceptibility assessment and the earthquake disaster prevention.

relevant vector machine  /  geneticalgorithms  /  earthquake-induced landslides  /  susceptibility assessment