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A maximal covering location-based model for analyzing the vulnerability of landscapes to wildfires: Assessing the worst-case scenario

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  • Rashidi, Eghbal
  • Medal, Hugh
  • Gordon, Jason
  • Grala, Robert
  • Varner, Morgan

Abstract

In this research, we study the vulnerability of landscapes to wildfires based on the impact of the worst-case scenario ignition locations. Using this scenario, we model wildfires that cause the largest damage to a landscape over a given time horizon. The landscape is modeled as a grid network, and the spread of wildfire is modeled using the minimum travel time model. To assess the impact of a wildfire in the worst-case scenario, we develop a mathematical programming model to optimally locate the ignition points so that the resulting wildfire results in the maximum damage. We compare the impacts of the worst-case wildfires (with optimally located ignition points) with the impacts of wildfires with randomly located ignition points on three landscape test cases clipped out from three national forests located in the western U.S. Our results indicate that the worst-case wildfires, on average, have more than twice the impact on landscapes than wildfires with randomly located ignition points.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:258:y:2017:i:3:p:1095-1105
    DOI: 10.1016/j.ejor.2016.08.074
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    References listed on IDEAS

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    1. Kim, Young-Hwan & Bettinger, Pete & Finney, Mark, 2009. "Spatial optimization of the pattern of fuel management activities and subsequent effects on simulated wildfires," European Journal of Operational Research, Elsevier, vol. 197(1), pages 253-265, August.
    2. Minas, James P. & Hearne, John W. & Martell, David L., 2014. "A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts," European Journal of Operational Research, Elsevier, vol. 232(2), pages 412-422.
    3. Dimopoulou, Maria & Giannikos, Ioannis, 2004. "Towards an integrated framework for forest fire control," European Journal of Operational Research, Elsevier, vol. 152(2), pages 476-486, January.
    4. Bettinger, Pete & Boston, Kevin & Kim, Young-Hwan & Zhu, Jianping, 2007. "Landscape-level optimization using tabu search and stand density-related forest management prescriptions," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1265-1282, January.
    5. Masashi Konoshima & Claire A. Montgomery & Heidi J. Albers & Jeffrey L. Arthur, 2008. "Spatial-Endogenous Fire Risk and Efficient Fuel Management and Timber Harvest," Land Economics, University of Wisconsin Press, vol. 84(3), pages 449-468.
    6. David L. Martell, 2007. "Forest Fire Management," International Series in Operations Research & Management Science, in: Andres Weintraub & Carlos Romero & Trond Bjørndal & Rafael Epstein & Jaime Miranda (ed.), Handbook Of Operations Research In Natural Resources, chapter 0, pages 489-509, Springer.
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    Cited by:

    1. Qin Liu & Tiange Shi, 2019. "Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    2. 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.
    3. 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.
    4. 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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