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Probability-Based Wildfire Risk Measure for Decision-Making

Author

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  • Adán Rodríguez-Martínez

    (Interdisciplinary Mathematics Institute, Complutense University of Madrid (UCM), 28040 Madrid, Spain)

  • Begoña Vitoriano

    (Interdisciplinary Mathematics Institute, Complutense University of Madrid (UCM), 28040 Madrid, Spain)

Abstract

Wildfire is a natural element of many ecosystems as well as a natural disaster to be prevented. Climate and land usage changes have increased the number and size of wildfires in the last few decades. In this situation, governments must be able to manage wildfire, and a risk measure can be crucial to evaluate any preventive action and to support decision-making. In this paper, a risk measure based on ignition and spread probabilities is developed modeling a forest landscape as an interconnected system of homogeneous sectors. The measure is defined as the expected value of losses due to fire, based on the probabilities of each sector burning. An efficient method based on Bayesian networks to compute the probability of fire in each sector is provided. The risk measure is suitable to support decision-making to compare preventive actions and to choose the best alternatives reducing the risk of a network. The paper is divided into three parts. First, we present the theoretical framework on which the risk measure is based, outlining some necessary properties of the fire probabilistic model as well as discussing the definition of the event ‘fire’. In the second part, we show how to avoid topological restrictions in the network and produce a computable and comprehensible wildfire risk measure. Finally, an illustrative case example is included.

Suggested Citation

  • Adán Rodríguez-Martínez & Begoña Vitoriano, 2020. "Probability-Based Wildfire Risk Measure for Decision-Making," Mathematics, MDPI, vol. 8(4), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:557-:d:343934
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    References listed on IDEAS

    as
    1. Mikael Rönnqvist & Sophie D’Amours & Andres Weintraub & Alejandro Jofre & Eldon Gunn & Robert Haight & David Martell & Alan Murray & Carlos Romero, 2015. "Operations Research challenges in forestry: 33 open problems," Annals of Operations Research, Springer, vol. 232(1), pages 11-40, September.
    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. James Minas & John Hearne & David Martell, 2015. "An integrated optimization model for fuel management and fire suppression preparedness planning," Annals of Operations Research, Springer, vol. 232(1), pages 201-215, September.
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    Cited by:

    1. Rosa Fernández Ropero & María Julia Flores & Rafael Rumí, 2022. "Bayesian Networks for Preprocessing Water Management Data," Mathematics, MDPI, vol. 10(10), pages 1-18, May.
    2. Laura Serra & Claudio Detotto & Marco Vannini, 2022. "Public lands as a mitigator of wildfire burned area using a spatio-temporal model applied in Sardinia," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 621-635, December.

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