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

Shreejit Poudyal (), Alex Lindquist, Nate Smullen, Victoria York, Ali Lotfi, James Greene and Mohammad Meysami
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Shreejit Poudyal: Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
Alex Lindquist: Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
Nate Smullen: Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA
Victoria York: Department of Mathematics, 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

J, 2024, vol. 7, issue 3, 1-15

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.

Keywords: California wildfires; environmental factors; wildfire dynamics; Bayesian methodologies; county-level analysis (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2024
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