Seismic human loss estimation for an earthquake disaster using neural network

Summary, in English

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.

• Författare: H. Aghamohammadi; M. S. Mesgari; A. Mansourian; D. Molaei
• Publiceringsår: 2013
• Språk: Engelska
• Sidor: 931-939
• Publikation/Tidskrift/Serie: International Journal of Environmental Science and Technology
• Volym: 10
• Issue: 5
• Länkar: http://dx.doi.org/10.1007/s13762-013-0281-5
• Dokumenttyp: Artikel i tidskrift
• Förlag: Center for Environmental and Energy Research and Studies
• Ämne: Engineering and Technology; Computer and Information Science; Geosciences, Multidisciplinary; Other Earth and Related Environmental Sciences
• Nyckelord: Back propagation; Building damage; Injuries; Rescue operation; Artificial neural network (ANN); Artificial Intelligence (AI)
• Status: Published
• ISBN/ISSN/Övrigt: ISSN: 1735-1472