IDEAS home Printed from https://ideas.repec.org/a/gam/jjopen/v7y2024i3p18-333d1459088.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-8800/7/3/18/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-8800/7/3/18/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjopen:v:7:y:2024:i:3:p:18-333:d:1459088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.