Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 18
Articles | Volume 20, issue 18
https://doi.org/10.5194/acp-20-11065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-11065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 18
Research article
 | 
28 Sep 2020
Research article |  | 28 Sep 2020

Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques

Sally S.-C. Wang and Yuxuan Wang

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Short summary
A model consisting of multiple machine learning algorithms is developed to predict wildfire burned area over the south central US and explains key environmental drivers. The developed model alleviates the issue of unevenly distributed data and predicts burned grids and burned areas with good accuracy. The model reveals climate variability such as relative humidity anomalies and antecedent drought severity contributes the most to the total burned area for winter–spring and summer fire season.
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