Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention

Research on Rapid Assessment of Earthquake Death Toll Based on Neural Network

Authors
Haoyu Wu, Xinyan Wu, Hongwei Li, Yong Luo
Corresponding Author
Haoyu Wu
Available Online November 2016.
DOI
10.2991/rac-16.2016.27How to use a DOI?
Keywords
death toll; BP neural network; earthquakes; rapid assessment
Abstract

101 post-1960 fatal earthquakes in China mainland were selected to build a BP neural network model for rapid earthquake fatality estimation. In this model, event time, magnitude, epicenter intensity, population density and the event region were selected as input vectors. While, the fatality was assigned as output vector. An empirical test shows that the BP neural network model can give a more accurate assessment, with a good match to the real death toll. This approach can be applied in rapid earthquake losses estimation, and provide scientific basis for emergency rescue and government decision-making.

Copyright
© 2016, 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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Volume Title
Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
10.2991/rac-16.2016.27
ISSN
1951-6851
DOI
10.2991/rac-16.2016.27How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Haoyu Wu
AU  - Xinyan Wu
AU  - Hongwei Li
AU  - Yong Luo
PY  - 2016/11
DA  - 2016/11
TI  - Research on Rapid Assessment of Earthquake Death Toll Based on Neural Network
BT  - Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention
PB  - Atlantis Press
SP  - 169
EP  - 174
SN  - 1951-6851
UR  - https://doi.org/10.2991/rac-16.2016.27
DO  - 10.2991/rac-16.2016.27
ID  - Wu2016/11
ER  -