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Sustainability 2019, 11(2), 306; https://doi.org/10.3390/su11020306

Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network

1
School of Information Technology and Network Security, People’s Public Security University of China, Beijing 102628, China
2
School of International Police Studies, People’s Public Security University of China, Beijing 102628, China
*
Author to whom correspondence should be addressed.
Received: 26 October 2018 / Revised: 26 December 2018 / Accepted: 2 January 2019 / Published: 9 January 2019
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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. View Full-Text
Keywords: risk analysis; dirty bomb; Bayesian network; terrorism risk analysis; dirty bomb; Bayesian network; terrorism
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Tang, Z.; Li, Y.; Hu, X.; Wu, H. Risk Analysis of Urban Dirty Bomb Attacking Based on Bayesian Network. Sustainability 2019, 11, 306.

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