Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California
Author:
Poudyal Shreejit12, Lindquist Alex12, Smullen Nate12, York Victoria12, Lotfi Ali3ORCID, Greene James1, Meysami Mohammad1ORCID
Affiliation:
1. Department of Mathematics, Clarkson University, Potsdam, NY 13699, USA 2. Department of Computer Science, Clarkson University, Potsdam, NY 13699, USA 3. Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
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.
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