Seismic Landslide Hazard Identification and Assessment Based on BP Neural Network
Jinsheng Fan, Weidong Li, Xinjian Shan
Available Online October 2015.
- https://doi.org/10.2991/seee-15.2015.45How to use a DOI?
- BP neural network; seismic landslide; geographic information systems; identification and assessment
- 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° ~ E105°, N30.8° ~ N32°) 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jinsheng Fan AU - Weidong Li AU - Xinjian Shan PY - 2015/10 DA - 2015/10 TI - Seismic Landslide Hazard Identification and Assessment Based on BP Neural Network BT - 2015 International Conference on Sustainable Energy and Environmental Engineering PB - Atlantis Press SP - 183 EP - 185 SN - 2352-5401 UR - https://doi.org/10.2991/seee-15.2015.45 DO - https://doi.org/10.2991/seee-15.2015.45 ID - Fan2015/10 ER -