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Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California

Author

Listed:
  • Shreejit Poudyal

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
    Department of Computer Science, Clarkson University, Potsdam, NY 13699, USA)

  • Alex Lindquist

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
    Department of Computer Science, Clarkson University, Potsdam, NY 13699, USA)

  • Nate Smullen

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
    Department of Computer Science, Clarkson University, Potsdam, NY 13699, USA)

  • Victoria York

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
    Department of Computer Science, Clarkson University, Potsdam, NY 13699, USA)

  • Ali Lotfi

    (Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada)

  • James Greene

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA)

  • Mohammad Meysami

    (Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA)

Abstract

Recently, the United States has experienced, on average, costs of USD 20 billion due to natural and climate disasters, such as hurricanes and wildfires. In this study, we focus on wildfires, which have occurred more frequently in the past few years. This paper examines how various factors, such as the PM10 levels, elevation, precipitation, SOX, population, and temperature, can influence the intensity of wildfires differently across counties in California. More specifically, we use Bayesian analysis to classify all counties of California into two groups: those with more wildfires and those with fewer wildfires. The Bayesian model incorporates prior knowledge and uncertainty for a more robust understanding of how these environmental factors impact wildfires differently among county groups. The findings show a similar effect of the SOX, population, and temperature, while the PM10, elevation, and precipitation have different implications for wildfires across various groups.

Suggested Citation

  • Shreejit Poudyal & Alex Lindquist & Nate Smullen & Victoria York & Ali Lotfi & James Greene & Mohammad Meysami, 2024. "Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California," J, MDPI, vol. 7(3), pages 1-15, August.
    • Handle: RePEc:gam:jjopen:v:7:y:2024:i:3:p:18-333:d:1459088
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    References listed on IDEAS

    as
    1. S. Tiwari & D. Chate & M. Srivastava & P. Safai & A. Srivastava & D. Bisht & B. Padmanabhamurty, 2012. "Statistical evaluation of PM 10 and distribution of PM 1 , PM 2.5 , and PM 10 in ambient air due to extreme fireworks episodes (Deepawali festivals) in megacity Delhi," 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. 61(2), pages 521-531, March.
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