Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1899-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1899-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
08 Mar 2022
Model description paper |
| 08 Mar 2022
| 08 Mar 2022
Building a machine learning surrogate model for wildfire activities within a global Earth system model
Qing Zhu
CORRESPONDING AUTHOR
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Fa Li
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
William J. Riley
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Li Xu
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
Department of Civil and Environmental Engineering, University of
Illinois Urbana-Champaign, Champaign, IL, USA
Kunxiaojia Yuan
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Huayi Wu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, China
James Randerson
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
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Cited
20 citations as recorded by crossref.
- Global fire modelling and control attributions based on the ensemble machine learning and satellite observations Y. Zhang et al. 10.1016/j.srs.2023.100088
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim 10.1088/1748-9326/ad8bdc
- ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model Y. Liu et al. 10.5194/gmd-18-4103-2025
- Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution M. Khan et al. 10.1016/j.jenvman.2024.121296
- Tree-ring based forest model calibrations with a deep learning algorithm X. Yu et al. 10.1016/j.foreco.2024.122154
- Advancements in Artificial Intelligence Applications for Forest Fire Prediction H. Liu et al. 10.3390/f16040704
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. 10.1109/TETCI.2023.3298535
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. 10.5194/gmd-16-4017-2023
- Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands T. Hüttnerová et al. 10.3390/s23042001
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. 10.1029/2023JD039154
- Forecasting Crop Residue Fires in Northeastern China Using Machine Learning B. Bai et al. 10.3390/atmos13101616
- Causal hybrid modeling with double machine learning—applications in carbon flux modeling K. Cohrs et al. 10.1088/2632-2153/ad5a60
- Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability F. Ji et al. 10.1016/j.rse.2025.114818
- A Review on Fire Research of Electric Power Grids of China: State-Of-The-Art and New Insights Z. Jiaqing et al. 10.1007/s10694-022-01343-x
- Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model R. Son et al. 10.1029/2023MS003710
- AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics F. Li et al. 10.5194/gmd-16-869-2023
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 10.1088/1748-9326/adad02
- Reimagine fire science for the anthropocene J. Shuman et al. 10.1093/pnasnexus/pgac115
- Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale S. Cheng et al. 10.1109/TETCI.2024.3445450
- Building a machine learning surrogate model for wildfire activities within a global Earth system model Q. Zhu et al. 10.5194/gmd-15-1899-2022
19 citations as recorded by crossref.
- Global fire modelling and control attributions based on the ensemble machine learning and satellite observations Y. Zhang et al. 10.1016/j.srs.2023.100088
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim 10.1088/1748-9326/ad8bdc
- ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model Y. Liu et al. 10.5194/gmd-18-4103-2025
- Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution M. Khan et al. 10.1016/j.jenvman.2024.121296
- Tree-ring based forest model calibrations with a deep learning algorithm X. Yu et al. 10.1016/j.foreco.2024.122154
- Advancements in Artificial Intelligence Applications for Forest Fire Prediction H. Liu et al. 10.3390/f16040704
- A Generative Model for Surrogates of Spatial-Temporal Wildfire Nowcasting S. Cheng et al. 10.1109/TETCI.2023.3298535
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. 10.5194/gmd-16-4017-2023
- Comparison of Individual Sensors in the Electronic Nose for Stress Detection in Forest Stands T. Hüttnerová et al. 10.3390/s23042001
- Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models S. Wang et al. 10.1029/2023JD039154
- Forecasting Crop Residue Fires in Northeastern China Using Machine Learning B. Bai et al. 10.3390/atmos13101616
- Causal hybrid modeling with double machine learning—applications in carbon flux modeling K. Cohrs et al. 10.1088/2632-2153/ad5a60
- Leveraging transfer learning and leaf spectroscopy for leaf trait prediction with broad spatial, species, and temporal applicability F. Ji et al. 10.1016/j.rse.2025.114818
- A Review on Fire Research of Electric Power Grids of China: State-Of-The-Art and New Insights Z. Jiaqing et al. 10.1007/s10694-022-01343-x
- Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model R. Son et al. 10.1029/2023MS003710
- AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics F. Li et al. 10.5194/gmd-16-869-2023
- Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions Q. Zhu et al. 10.1088/1748-9326/adad02
- Reimagine fire science for the anthropocene J. Shuman et al. 10.1093/pnasnexus/pgac115
- Deep Learning Surrogate Models of JULES-INFERNO for Wildfire Prediction on a Global Scale S. Cheng et al. 10.1109/TETCI.2024.3445450
1 citations as recorded by crossref.
Latest update: 10 Jul 2025
Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Wildfire is a devastating Earth system process that burns about 500 million hectares of land...
