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An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression

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  • Eghbal Rashidi
  • Hugh Medal
  • Aaron Hoskins

Abstract

Wildfire managers use initial attack (IA) to control wildfires before they grow large and become difficult to suppress. Although the majority of wildfire incidents are contained by IA, the small percentage of fires that escape IA causes most of the damage. Therefore, planning a successful IA is very important. In this article, we study the vulnerability of IA in wildfire suppression using an attacker‐defender Stackelberg model. The attacker's objective is to coordinate the simultaneous ignition of fires at various points in a landscape to maximize the number of fires that cannot be contained by IA. The defender's objective is to optimally dispatch suppression resources from multiple fire stations located across the landscape to minimize the number of wildfires not contained by IA. We use a decomposition algorithm to solve the model and apply the model on a test case landscape. We also investigate the impact of delay in the response, the fire growth rate, the amount of suppression resources, and the locations of fire stations on the success of IA.

Suggested Citation

  • Eghbal Rashidi & Hugh Medal & Aaron Hoskins, 2018. "An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(2), pages 120-134, March.
  • Handle: RePEc:wly:navres:v:65:y:2018:i:2:p:120-134
    DOI: 10.1002/nav.21792
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    References listed on IDEAS

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    1. Nedialko B. Dimitrov & David P. Morton, 2013. "Interdiction Models and Applications," International Series in Operations Research & Management Science, in: Jeffrey W. Herrmann (ed.), Handbook of Operations Research for Homeland Security, edition 127, chapter 0, pages 73-103, Springer.
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    3. Rashidi, Eghbal & Medal, Hugh & Gordon, Jason & Grala, Robert & Varner, Morgan, 2017. "A maximal covering location-based model for analyzing the vulnerability of landscapes to wildfires: Assessing the worst-case scenario," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1095-1105.
    4. Aaron B. Hoskins & Hugh R. Medal & Eghbal Rashidi, 2017. "Satellite constellation design for forest fire monitoring via a stochastic programing approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(8), pages 642-661, December.
    5. Nada Petrovic & David L Alderson & Jean M Carlson, 2012. "Dynamic Resource Allocation in Disaster Response: Tradeoffs in Wildfire Suppression," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
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    Cited by:

    1. Karwowski, Jan & Mańdziuk, Jacek, 2019. "A Monte Carlo Tree Search approach to finding efficient patrolling schemes on graphs," European Journal of Operational Research, Elsevier, vol. 277(1), pages 255-268.
    2. Avci, Mualla Gonca & Avci, Mustafa & Battarra, Maria & Erdoğan, Güneş, 2024. "The wildfire suppression problem with multiple types of resources," European Journal of Operational Research, Elsevier, vol. 316(2), pages 488-502.
    3. Bhuiyan, Tanveer Hossain & Moseley, Maxwell C. & Medal, Hugh R. & Rashidi, Eghbal & Grala, Robert K., 2019. "A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior," European Journal of Operational Research, Elsevier, vol. 277(2), pages 699-718.

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