Seismic Landslide Hazard Identification and Assessment Based on BP Neural Network

DOI

10.2991/seee-15.2015.45How to use a DOI?

Keywords

BP neural network; seismic landslide; geographic information systems; identification and assessment

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/seee-15.2015.45
ID  - Fan2015/10
ER  -