Abstract
The probability of the occurrence of coseismic landslides is basically blank. In this study, the Bayesian Probability Method and the Machine Model are used to carry out the real probability of coseismic landslides of China. The first generation of coseismic landslide hazard probability map of China is produced on the basis of nine earthquake cases. They include 1999 Chi-chi, Taiwan, 2005 Kashmir, 2008 Wenchuan, 2010 Yushu, 2013 Lushan, 2013 Minxian, 2014 Ludian, 2015 Nepal, and 2017 Jiuzhaigou earthquakes. Seven of the nine earthquakes occurred in China. The 2005 Kashmir and the 2015 Nepal quakes occurred in China's neighboring areas, which can better control the accuracy of the model. All these earthquake events have detailed and complete coseismic landslide inventories. They include 306 435 landslide polygons. Considering the real earthquake landslide occurrence area, the difference of landslide size, the ratio of landslide to non-slip sample ratio, a total of 5 117 000 samples are selected. A total of 13 factors are selected. They are absolute elevation, relative elevation, slope angle, slope aspect, slope curvature, slope position, topographic humidity index, land cover, vegetation coverage percentage, fault distance, stratum, average annual precipitation, and peak ground acceleration. The Bayesian probability method is combined with the machine learning model to establish a multi-factor impact model for the probability of earthquake-triggered landslide. Then the weights of each continuous factor and the weight of each class of the classification factor are obtained. The model is applied in China considering the peak ground acceleration as the triggering factor of landslides and considering the real probability of earthquake landslides in China under different peak ground accelerations(0.1~1 g, one result per 0.1 g, a total of 10 results). In addition, combined with Seismic Ground Motion Parameters Zonation Map of China, the corresponding true probability of earthquake-triggered landslides of China is generated.
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