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Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network

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  • Khakzad, Nima

Abstract

Global warming and the subsequent increase in the frequency and severity of wildfires demand for specialized risk assessment and management methodologies to cope with the ever-increasing risk of wildfires in wildland-industrial interfaces (WIIs). Wildfires can jeopardize the safety and integrity of industrial plants, and trigger secondary fires and explosions especially in the case of process plants where large inventory of combustible and flammable substances is present. In the present study, by modeling the WII as a two dimensional lattice, we have developed an innovative methodology for modeling and assessing the risk of wildfire spread in WIIs by combining dynamic Bayesian network and wildfire behavior prediction models. The developed methodology models the spatial and temporal spread of fire, based on the most probable path of fire, both in the wildland and in the industrial area.

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  • Khakzad, Nima, 2019. "Modeling wildfire spread in wildland-industrial interfaces using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 165-176.
    • Handle: RePEc:eee:reensy:v:189:y:2019:i:c:p:165-176
    DOI: 10.1016/j.ress.2019.04.006
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    References listed on IDEAS

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    1. Nima Khakzad & Faisal Khan & Paul Amyotte & Valerio Cozzani, 2013. "Domino Effect Analysis Using Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 33(2), pages 292-306, February.
    2. M. D. Flannigan & B. M. Wotton & G. A. Marshall & W. J. de Groot & J. Johnston & N. Jurko & A. S. Cantin, 2016. "Fuel moisture sensitivity to temperature and precipitation: climate change implications," Climatic Change, Springer, vol. 134(1), pages 59-71, January.
    3. M. Flannigan & B. Wotton & G. Marshall & W. de Groot & J. Johnston & N. Jurko & A. Cantin, 2016. "Fuel moisture sensitivity to temperature and precipitation: climate change implications," Climatic Change, Springer, vol. 134(1), pages 59-71, January.
    4. Joe Scott & Don Helmbrecht & Matthew Thompson & David Calkin & Kate Marcille, 2012. "Probabilistic assessment of wildfire hazard and municipal watershed exposure," 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. 64(1), pages 707-728, October.
    5. Drossel, B. & Schwabl, F., 1992. "Self-organized criticality in a forest-fire model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 191(1), pages 47-50.
    6. Stepanov, Alexander & Smith, James MacGregor, 2012. "Modeling wildfire propagation with Delaunay triangulation and shortest path algorithms," European Journal of Operational Research, Elsevier, vol. 218(3), pages 775-788.
    7. Khakzad, Nima, 2015. "Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 263-272.
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    Cited by:

    1. Yang, Yunfeng & Chen, Guohua & Reniers, Genserik, 2020. "Vulnerability assessment of atmospheric storage tanks to floods based on logistic regression," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
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    3. Wu, Jiansong & Bai, Yiping & Fang, Weipeng & Zhou, Rui & Reniers, Genserik & Khakzad, Nima, 2021. "An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Lan, Meng & Gardoni, Paolo & Qin, Rongshui & Zhang, Xiao & Zhu, Jiping & Lo, Siuming, 2022. "Modeling NaTech-related domino effects in process clusters: A network-based approach," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    5. Yunfeng Yang & Guohua Chen & Yuanfei Zhao, 2023. "A Quantitative Framework for Propagation Paths of Natech Domino Effects in Chemical Industrial Parks: Part II—Risk Assessment and Mitigation System," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    6. Caratozzolo, Vincenzo & Misuri, Alessio & Cozzani, Valerio, 2022. "A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    7. Yunfeng Yang & Guohua Chen & Yuanfei Zhao, 2023. "A Quantitative Framework for Propagation Paths of Natech Domino Effects in Chemical Industrial Parks: Part I—Failure Analysis," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    8. Naderpour, Mohsen & Rizeei, Hossein Mojaddadi & Khakzad, Nima & Pradhan, Biswajeet, 2019. "Forest fire induced Natech risk assessment: A survey of geospatial technologies," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Ye Zheng & Yazhou Xie & Xuejiao Long, 2021. "A comprehensive review of Bayesian statistics in natural hazards engineering," 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. 108(1), pages 63-91, August.
    10. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2021. "Assessment of risk modification due to safety barrier performance degradation in Natech events," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    11. Khakzad, Nima & Cozzani, Valerio, 2020. "Special issue: Quantitative assessment and risk management of Natech accidents," Reliability Engineering and System Safety, Elsevier, vol. 203(C).

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