IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i6p2051-d334625.html
   My bibliography  Save this article

Modeling and Risk Analysis of Chemical Terrorist Attacks: A Bayesian Network Method

Author

Listed:
  • Rongchen Zhu

    (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)

  • Xin Li

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

  • Han Ye

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

  • Nan Jia

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

Abstract

The chemical terrorist attack is an unconventional form of terrorism with vast scope of influence, strong concealment, high technical means and severe consequences. Chemical terrorism risk refers to the uncertainty of the effects of terrorist organisations using toxic industrial chemicals/drugs and classic chemical weapons to attack the population. There are multiple risk factors infecting chemical terrorism risk, such as the threat degree of terrorist organisations, attraction of targets, city emergency response capabilities, and police defense capabilities. We have constructed a Bayesian network of chemical terrorist attacks to conduct risk analysis. The scenario analysis and sensitivity analysis are applied to validate the model and analyse the impact of the vital factor on the risk of chemical terrorist attacks. The results show that the model can be used for simulation and risk analysis of chemical terrorist attacks. In terms of controlling the risk of chemical terrorist attack, patrol and surveillance are less critical than security checks and police investigations. Security check is the most effective approach to decrease the probability of successful attacks. Different terrorist organisations have different degrees of threat, but the impacts of which are limited to the success of the attack. Weapon types and doses are sensitive to casualties, but it is the level of emergency response capabilities that dominates the changes in casualties. Due to the limited number of defensive resources, to get the best consequence, the priority of the deployment of defensive sources should be firstly given to governmental buildings, followed by commercial areas. These findings may provide the theoretical basis and method support for the combat of the public security department and the safety prevention decision of the risk management department.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:6:p:2051-:d:334625
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/6/2051/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/6/2051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rezazadeh, Amirali & Talarico, Luca & Reniers, Genserik & Cozzani, Valerio & Zhang, Laobing, 2019. "Applying game theory for securing oil and gas pipelines against terrorism," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. 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.
    3. Samrat Chatterjee & Mark D. Abkowitz, 2011. "A Methodology for Modeling Regional Terrorism Risk," Risk Analysis, John Wiley & Sons, vol. 31(7), pages 1133-1140, July.
    4. Kleemann, Janina & Celio, Enrico & Fürst, Christine, 2017. "Validation approaches of an expert-based Bayesian Belief Network in Northern Ghana, West Africa," Ecological Modelling, Elsevier, vol. 365(C), pages 10-29.
    5. Henry H. Willis & Tom LaTourrette, 2008. "Using Probabilistic Terrorism Risk Modeling for Regulatory Benefit‐Cost Analysis: Application to the Western Hemisphere Travel Initiative in the Land Environment," Risk Analysis, John Wiley & Sons, vol. 28(2), pages 325-339, April.
    6. 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.
    7. Jones, B. & Jenkinson, I. & Yang, Z. & Wang, J., 2010. "The use of Bayesian network modelling for maintenance planning in a manufacturing industry," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 267-277.
    8. Barry Charles Ezell & Steven P. Bennett & Detlof Von Winterfeldt & John Sokolowski & Andrew J. Collins, 2010. "Probabilistic Risk Analysis and Terrorism Risk," Risk Analysis, John Wiley & Sons, vol. 30(4), pages 575-589, April.
    9. Monroe, Jacob & Ramsey, Elizabeth & Berglund, Emily, 2018. "Allocating countermeasures to defend water distribution systems against terrorist attack," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 37-51.
    10. Walter W. Piegorsch & Susan L. Cutter & Frank Hardisty, 2007. "Benchmark Analysis for Quantifying Urban Vulnerability to Terrorist Incidents," Risk Analysis, John Wiley & Sons, vol. 27(6), pages 1411-1425, December.
    11. 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.
    12. Gregory L. Keeney & Detlof von Winterfeldt, 2010. "Identifying and Structuring the Objectives of Terrorists," Risk Analysis, John Wiley & Sons, vol. 30(12), pages 1803-1816, December.
    13. Russell L. Bennett, 2006. "Chemical or Biological Terrorist Attacks: An Analysis of the Preparedness of Hospitals for Managing Victims Affected by Chemical or Biological Weapons of Mass Destruction," IJERPH, MDPI, vol. 3(1), pages 1-9, March.
    14. Argenti, Francesca & Landucci, Gabriele & Reniers, Genserik & Cozzani, Valerio, 2018. "Vulnerability assessment of chemical facilities to intentional attacks based on Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 515-530.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunmeng Lu & Tiantian Wang & Tiezhong Liu, 2020. "Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents," IJERPH, MDPI, vol. 17(15), pages 1-20, July.
    2. Yoshiaki Nomura & Ryoko Otsuka & Wit Yee Wint & Ayako Okada & Ryo Hasegawa & Nobuhiro Hanada, 2020. "Tooth-Level Analysis of Dental Caries in Primary Dentition in Myanmar Children," IJERPH, MDPI, vol. 17(20), pages 1-13, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mark G. Stewart & John Mueller, 2013. "Terrorism Risks and Cost‐Benefit Analysis of Aviation Security," Risk Analysis, John Wiley & Sons, vol. 33(5), pages 893-908, May.
    2. Christoph Werner & Tim Bedford & John Quigley, 2018. "Sequential Refined Partitioning for Probabilistic Dependence Assessment," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2683-2702, December.
    3. G. Quijano, Eduardo & Ríos Insua, David & Cano, Javier, 2018. "Critical networked infrastructure protection from adversaries," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 27-36.
    4. Kabir, Golam & Balek, Ngandu Balekelayi Celestin & Tesfamariam, Solomon, 2018. "Consequence-based framework for buried infrastructure systems: A Bayesian belief network model," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 290-301.
    5. Jason R. W. Merrick & Philip Leclerc, 2016. "Modeling Adversaries in Counterterrorism Decisions Using Prospect Theory," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 681-693, April.
    6. Sumitra Sri Bhashyam & Gilberto Montibeller, 2016. "In the Opponent's Shoes: Increasing the Behavioral Validity of Attackers’ Judgments in Counterterrorism Models," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 666-680, April.
    7. Dong, Mingxin & Zhang, Zhen & Liu, Yi & Zhao, Dong Feng & Meng, Yifei & Shi, Jihao, 2023. "Playing Bayesian Stackelberg game model for optimizing the vulnerability level of security incident system in petrochemical plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    8. Iaiani, Matteo & Casson Moreno, Valeria & Reniers, Genserik & Tugnoli, Alessandro & Cozzani, Valerio, 2021. "Analysis of events involving the intentional release of hazardous substances from industrial facilities," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    9. E. S. Levine, 2012. "Estimating Conditional Probabilities of Terrorist Attacks: Modeling Adversaries with Uncertain Value Tradeoffs," Risk Analysis, John Wiley & Sons, vol. 32(2), pages 294-303, February.
    10. Iaiani, Matteo & Sorichetti, Riccardo & Tugnoli, Alessandro & Cozzani, Valerio, 2024. "Modelling standoff distances to prevent escalation in shooting attacks to tanks storing hazardous materials," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    11. Chuan Wang & Yupeng Liu & Wen Hou & Chao Yu & Guorong Wang & Yuyan Zheng, 2021. "Reliability and availability modeling of Subsea Autonomous High Integrity Pressure Protection System with partial stroke test by Dynamic Bayesian," Journal of Risk and Reliability, , vol. 235(2), pages 268-281, April.
    12. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
    13. Di Zhang & Xinping Yan & Zaili Yang & Jin Wang, 2014. "An accident data–based approach for congestion risk assessment of inland waterways: A Yangtze River case," Journal of Risk and Reliability, , vol. 228(2), pages 176-188, April.
    14. Wu, Jiansong & Zhang, Linlin & Bai, Yiping & Reniers, Genserik, 2022. "A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    15. Gainbi Park & Zengwang Xu, 2022. "The constituent components and local indicator variables of social vulnerability index," 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. 110(1), pages 95-120, January.
    16. Li, Mei & Liu, Zixian & Li, Xiaopeng & Liu, Yiliu, 2019. "Dynamic risk assessment in healthcare based on Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 327-334.
    17. Hassan, Shamsu & Wang, Jin & Kontovas, Christos & Bashir, Musa, 2022. "An assessment of causes and failure likelihood of cross-country pipelines under uncertainty using bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    18. Uflaz, Esma & Sezer, Sukru Ilke & Tunçel, Ahmet Lutfi & Aydin, Muhammet & Akyuz, Emre & Arslan, Ozcan, 2024. "Quantifying potential cyber-attack risks in maritime transportation under Dempster–Shafer theory FMECA and rule-based Bayesian network modelling," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    19. Yanling Chang & Alan Erera & Chelsea White, 2015. "Value of information for a leader–follower partially observed Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 129-153, December.
    20. Li, Yapeng & Qiao, Shun & Deng, Ye & Wu, Jun, 2019. "Stackelberg game in critical infrastructures from a network science perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 705-714.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:6:p:2051-:d:334625. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.