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Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network

Author

Listed:
  • Zheng Tang

    (School of Information Technology and Network Security, People’s Public Security University of China, Beijing 102628, China)

  • Yijia Li

    (School of Information Technology and Network Security, People’s Public Security University of China, Beijing 102628, China)

  • Xiaofeng Hu

    (School of Information Technology and Network Security, People’s Public Security University of China, Beijing 102628, China)

  • Huanggang Wu

    (School of International Police Studies, People’s Public Security University of China, Beijing 102628, China)

Abstract

Urban dirty bomb attacking is a type of unconventional terrorism threatening the urban security all through the world. In this paper, a Bayesian network of urban dirty bomb attacking is established to analyze the risk of urban dirty bomb attacking. The impacts of factors such as occurrence time, location, wind fields, the size of dirty bomb, emergency response and defense approaches on casualty from both direct blast and radiation-caused cancers are examined. Results show that sensitivity of casualty from cancers to wind fields are less significant; the impact of emergency response on the direct casualty from blast is not large; the size of the dirty bomb results in more casualties from cancers than that from bomb explosions; Whether an attack is detected by the police is not that related to normal or special time, but significantly depends on the attack location; Furthermore, casualty from cancers significantly depends on the location, while casualty from blast is not considerably influenced by the attacking location; patrol and surveillance are less important than security check in terms of controlling the risk of urban dirt bomb, and security check is the most effective approach to decreasing the risk of urban dirty bomb.

Suggested Citation

  • Zheng Tang & Yijia Li & Xiaofeng Hu & Huanggang Wu, 2019. "Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:306-:d:196142
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    References listed on IDEAS

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    1. H. Rosoff & D. Von Winterfeldt, 2007. "A Risk and Economic Analysis of Dirty Bomb Attacks on the Ports of Los Angeles and Long Beach," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 533-546, June.
    2. Zhi Yuan & Nima Khakzad & Faisal Khan & Paul Amyotte, 2015. "Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 35(2), pages 278-291, February.
    3. Francis, Royce A. & Guikema, Seth D. & Henneman, Lucas, 2014. "Bayesian Belief Networks for predicting drinking water distribution system pipe breaks," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 1-11.
    4. Cai, Baoping & Liu, Yu & Fan, Qian, 2016. "A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 105-115.
    5. Chao Zhang & Jiansong Wu & Chao Huang & Bo Jiang, 2018. "A model for the representation of emergency cases," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 337-351, March.
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    Cited by:

    1. Xiao Zhang & Xiaofeng Hu & Yiping Bai & Jiansong Wu, 2020. "Risk Assessment of Gas Leakage from School Laboratories Based on the Bayesian Network," IJERPH, MDPI, vol. 17(2), pages 1-18, January.
    2. Lina Han & Qing Ma & Feng Zhang & Yichen Zhang & Jiquan Zhang & Yongbin Bao & Jing Zhao, 2019. "Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models," IJERPH, MDPI, vol. 16(18), pages 1-17, September.
    3. Rongchen Zhu & Xin Li & Xiaofeng Hu & Deshui Hu, 2019. "Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network," Sustainability, MDPI, vol. 12(1), pages 1-20, December.
    4. Rongchen Zhu & Xiaofeng Hu & Xin Li & Han Ye & Nan Jia, 2020. "Modeling and Risk Analysis of Chemical Terrorist Attacks: A Bayesian Network Method," IJERPH, MDPI, vol. 17(6), pages 1-23, March.

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