Post-Earthquake Fire Risk Decision Research Based on Bayesian Networks

Conference paper
First Online:

Abstract

This paper applies the Bayesian Networks in post-earthquake fire risk decision. The model based on Bayesian Networks is proposed. Post-earthquake fire risk model is presented for analyzing risk and possible economic losses, which is used for disaster prevention and reduction decision supporting. In the end, the paper gives a particular explanation of post-earthquake fire risk model for knowledge discovery and decision-making in order to provide some references to earthquake rescue and fire forces.

Keywords

Bayesian networks Distribution of factors Discretization Fire risk Post-earthquake 
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Notes

Acknowledgments

This work is supported by Science and Technology Supporting Subject “The Research on Across-district Force Dispatch on Fireground and organization of Command Technology” Grant No. 2006BAK06B05.

References

  1. Chen R, Sivakumar K, Kargupta H (2004) Collective mining of Bayesian networks from distributed heterogeneous data. Knowledge and Information Systems: 164–187Google Scholar
  2. Gowdy JN, Subramanya A, Bartels C (2004) DBN based multi-stream models for audio-visual speech recognition. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 1, pp 993–998Google Scholar
  3. Kampke T, Elfes A (2001) A bayesian network approach to sensor fusion of aerial imagery, pro sensor fusion and decentralized control in robotic system IV, pp 50–64Google Scholar
  4. Lu W (1995) Forcasting post-earthquake fire loss using neural networks. Tongji Univ J 23(1):15–20Google Scholar
  5. Rahimi A, Darrell T (2002) Bayesian network for online global pose estimation. IEEE/RSJ international conference on intelligent robots and system, vol 1, pp 427–433Google Scholar
  6. Stephenson TA, Escofet J, Magimai-Doss M (2002) Dynamic Bayesian nework based speech recognition with pitch and energy as auxiliary variables. The 12th IEEE Workshop on Neural Networks for Signal Processing, pp 637–646Google Scholar
  7. Tian F, Lu Y (2004) A DBN inference algorithm using junction tree. Intel Control Autom 5:4236–4240Google Scholar
  8. Wellman MP (2000) Distributed decision making and plan recognition under uncertainty. ADA405436Google Scholar
  9. Wong ML, Lam W, Leung KS (1999) Using evolutionary programming and minimum description length principle for data mining of Bayesian networks. IEEE Trans Pattern Anal Mach Intell 21(2):174–178CrossRefGoogle Scholar
  10. Zhu Q (2002) nonmonotonic extrapolation of causal relation for knowledge-based decision support using a Bayesian network approach, AFRL-SR-AR-TR-02-0423. Rauch, John T Jr, Assessing Airpowers Effectes: capabilities and Limitations of Real-Time Battle Damage Assessment. ADA420587Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Energy and SafetyAnhui University of Science and TechnologyLangfangChina

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