Articles | Volume 20, issue 18
https://doi.org/10.5194/acp-20-11065-2020
https://doi.org/10.5194/acp-20-11065-2020
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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Cited articles

Amatulli, G., Camia, A., and San-Miguel-Ayanz, J.: Estimating future burned areas under changing climate in the EU-Mediterranean countries, Sci. Total Environ., 450/451, 209–222, https://doi.org/10.1016/j.scitotenv.2013.02.014, 2013. 
An, H., Gan, J., and Cho, S. J.: Assessing Climate Change Impacts on Wildfire Risk in the United States, Forests, 6, 3197–3211, https://doi.org/10.3390/f6093197, 2015. 
Baboo, S. and Devi, R.: An Analysis of Different Resampling Methods in Coimbatore, District, Global Journal of Computer Science and Technology, 10, 61–66, 2010. 
Balshi, M. S., McGUIRE, A. D., Duffy, P., Flannigan, M., Walsh, J., and Melillo, J.: Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach, Glob. Change Biol., 15, 578–600, https://doi.org/10.1111/j.1365-2486.2008.01679.x, 2009. 
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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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