Proceedings of the 2015 International Conference on Sustainable Energy and Environmental Engineering

Seismic Landslide Hazard Identification and Assessment Based on BP Neural Network

Authors
Jinsheng Fan, Weidong Li, Xinjian Shan
Corresponding Author
Jinsheng Fan
Available Online October 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 International Conference on Sustainable Energy and Environmental Engineering
Part of series
Advances in Engineering Research
Publication Date
October 2015
ISBN
978-94-6252-119-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/seee-15.2015.45How to use a DOI?
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
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  -