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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Sep 2020
Research article |
| 28 Sep 2020
| 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
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, Texas 77024, USA
now at: Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
Department of Earth and Atmospheric Sciences, University of Houston,
Houston, Texas 77024, USA
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Cited
16 citations as recorded by crossref.
- Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms X. Lin et al. 10.1007/s00376-024-3198-7
- Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain M. Bugallo et al. 10.1016/j.jenvman.2022.116788
- Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes R. Chen et al. 10.1016/j.jenvman.2023.120005
- Analysis of Trends in the Distance of Wildfires from Built-Up Areas in Spain and California (USA): 2007–2015 M. Marey-Perez et al. 10.3390/f15050788
- Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin R. Abolafia‐Rosenzweig et al. 10.1002/hyp.15314
- Land, jet stream, and other atmospheric effects on burned area estimation during the South Asian heatwave of 2022 A. Irawan et al. 10.1016/j.jag.2024.103720
- High-resolution mapping of wildfire drivers in California based on machine learning L. Qiu et al. 10.1016/j.scitotenv.2022.155155
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0 R. Tang et al. 10.5194/gmd-17-1525-2024
- Integrating hydrological parameters in wildfire risk assessment: a machine learning approach for mapping wildfire probability M. Khodaee et al. 10.1088/1748-9326/ad80ad
- Future fire-smoke PM2.5 health burden under climate change in Paraguay N. Borchers-Arriagada et al. 10.1016/j.scitotenv.2024.171356
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. 10.1029/2023JD039154
- Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation S. Wang et al. 10.1029/2020EF001910
- Integrating machine learning for enhanced wildfire severity prediction: A study in the Upper Colorado River basin H. Han et al. 10.1016/j.scitotenv.2024.175914
- Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area R. Abolafia-Rosenzweig et al. 10.1088/1748-9326/ac6886
- SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States J. Buch et al. 10.5194/gmd-16-3407-2023
- Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China R. Chen et al. 10.1007/s13753-023-00476-z
16 citations as recorded by crossref.
- Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms X. Lin et al. 10.1007/s00376-024-3198-7
- Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain M. Bugallo et al. 10.1016/j.jenvman.2022.116788
- Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes R. Chen et al. 10.1016/j.jenvman.2023.120005
- Analysis of Trends in the Distance of Wildfires from Built-Up Areas in Spain and California (USA): 2007–2015 M. Marey-Perez et al. 10.3390/f15050788
- Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin R. Abolafia‐Rosenzweig et al. 10.1002/hyp.15314
- Land, jet stream, and other atmospheric effects on burned area estimation during the South Asian heatwave of 2022 A. Irawan et al. 10.1016/j.jag.2024.103720
- High-resolution mapping of wildfire drivers in California based on machine learning L. Qiu et al. 10.1016/j.scitotenv.2022.155155
- Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0 R. Tang et al. 10.5194/gmd-17-1525-2024
- Integrating hydrological parameters in wildfire risk assessment: a machine learning approach for mapping wildfire probability M. Khodaee et al. 10.1088/1748-9326/ad80ad
- Future fire-smoke PM2.5 health burden under climate change in Paraguay N. Borchers-Arriagada et al. 10.1016/j.scitotenv.2024.171356
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. 10.1029/2023JD039154
- Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation S. Wang et al. 10.1029/2020EF001910
- Integrating machine learning for enhanced wildfire severity prediction: A study in the Upper Colorado River basin H. Han et al. 10.1016/j.scitotenv.2024.175914
- Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area R. Abolafia-Rosenzweig et al. 10.1088/1748-9326/ac6886
- SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States J. Buch et al. 10.5194/gmd-16-3407-2023
- Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China R. Chen et al. 10.1007/s13753-023-00476-z
Latest update: 20 Nov 2024
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.
A model consisting of multiple machine learning algorithms is developed to predict wildfire...
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