12 citations as recorded by crossref.

1. Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms X. Lin et al. 10.1007/s00376-024-3198-7
2. Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain M. Bugallo et al. 10.1016/j.jenvman.2022.116788
3. 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
4. Future fire-smoke PM2.5 health burden under climate change in Paraguay N. Borchers-Arriagada et al. 10.1016/j.scitotenv.2024.171356
5. Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. 10.1029/2023JD039154
6. Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation S. Wang et al. 10.1029/2020EF001910
7. 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
8. 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
9. Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China R. Chen et al. 10.1007/s13753-023-00476-z
10. 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
11. High-resolution mapping of wildfire drivers in California based on machine learning L. Qiu et al. 10.1016/j.scitotenv.2022.155155
12. 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