Advanced

Seismic human loss estimation for an earthquake disaster using neural network

Aghamohammadi, H.; Mesgari, M. S.; Mansourian, A. LU and Molaei, D. (2013) In International Journal of Environmental Science and Technology 10(5). p.931-939
Abstract

In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network's capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were... (More)

In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network's capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation.

(Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Back propagation, Building damage, Injuries, Rescue operation
in
International Journal of Environmental Science and Technology
volume
10
issue
5
pages
9 pages
publisher
Center for Environmental and Energy Research and Studies
external identifiers
  • scopus:84891639286
ISSN
1735-1472
DOI
10.1007/s13762-013-0281-5
language
English
LU publication?
no
id
d3c4790d-39e4-4a1e-8cf1-fe0477cc96b6
date added to LUP
2016-05-27 15:34:22
date last changed
2018-04-22 04:20:06
@article{d3c4790d-39e4-4a1e-8cf1-fe0477cc96b6,
  abstract     = {<p>In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network's capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation.</p>},
  author       = {Aghamohammadi, H. and Mesgari, M. S. and Mansourian, A. and Molaei, D.},
  issn         = {1735-1472},
  keyword      = {Back propagation,Building damage,Injuries,Rescue operation},
  language     = {eng},
  number       = {5},
  pages        = {931--939},
  publisher    = {Center for Environmental and Energy Research and Studies},
  series       = {International Journal of Environmental Science and Technology},
  title        = {Seismic human loss estimation for an earthquake disaster using neural network},
  url          = {http://dx.doi.org/10.1007/s13762-013-0281-5},
  volume       = {10},
  year         = {2013},
}