Seismic Landslide Hazard Identification and Assessment Based on BP Neural Network

Based on geographic information systems and remote sensing technology, this article used BP neural network method and choose slope, aspect, intensity, faults, water, elevation, DEM, hardness 8 earthquake landslide factors as influencing factors in the study area (E103 degrees similar to E105 degrees, N30.8 degrees similar to N32 degrees) to identify the earthquake and landslide-prone evaluation studies. The results show: BP neural network landslide recognition correct rate reached 85.3%, and 70% of the landslide occurred in the predicted high-risk areas, and the evaluation of seismic landslide convex curve showing a steep trend, the using of BP neural network is feasible to evaluate the seismic landslide.