Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Online Access: | https://www.mdpi.com/2571-8800/7/3/18 |
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| author | Shreejit Poudyal Alex Lindquist Nate Smullen Victoria York Ali Lotfi James Greene Mohammad Meysami |
| author_facet | Shreejit Poudyal Alex Lindquist Nate Smullen Victoria York Ali Lotfi James Greene Mohammad Meysami |
| author_sort | Shreejit Poudyal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-982acdd42403482da0952fe80dcbd363 |
| institution | OA Journals |
| issn | 2571-8800 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | J |
| spelling | doaj-art-982acdd42403482da0952fe80dcbd3632025-08-20T01:55:34ZengMDPI AGJ2571-88002024-08-017331933310.3390/j7030018Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in CaliforniaShreejit Poudyal0Alex Lindquist1Nate Smullen2Victoria York3Ali Lotfi4James Greene5Mohammad Meysami6Department of Mathematics, Clarkson University, Potsdam, NY 13699, USADepartment of Mathematics, Clarkson University, Potsdam, NY 13699, USADepartment of Mathematics, Clarkson University, Potsdam, NY 13699, USADepartment of Mathematics, Clarkson University, Potsdam, NY 13699, USADepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaDepartment of Mathematics, Clarkson University, Potsdam, NY 13699, USADepartment of Mathematics, Clarkson University, Potsdam, NY 13699, USARecently, 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.https://www.mdpi.com/2571-8800/7/3/18California wildfiresenvironmental factorswildfire dynamicsBayesian methodologiescounty-level analysis |
| spellingShingle | Shreejit Poudyal Alex Lindquist Nate Smullen Victoria York Ali Lotfi James Greene Mohammad Meysami Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California J California wildfires environmental factors wildfire dynamics Bayesian methodologies county-level analysis |
| title | Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California |
| title_full | Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California |
| title_fullStr | Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California |
| title_full_unstemmed | Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California |
| title_short | Unveiling Wildfire Dynamics: A Bayesian County-Specific Analysis in California |
| title_sort | unveiling wildfire dynamics a bayesian county specific analysis in california |
| topic | California wildfires environmental factors wildfire dynamics Bayesian methodologies county-level analysis |
| url | https://www.mdpi.com/2571-8800/7/3/18 |
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