Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-869-2023
© Author(s) 2023. 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-16-869-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Feb 2023
Model description paper |
| 03 Feb 2023
| 03 Feb 2023
AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics
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
Qing Zhu
CORRESPONDING AUTHOR
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
William J. Riley
Climate and Ecosystem Sciences Division, Climate Sciences Department, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Civil and Environmental Engineering, University of
Illinois Urbana-Champaign, Champaign, IL, USA
Li Xu
Department of Earth System Science, University of California Irvine, Irvine, CA, 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
Min Chen
Department of Forest and Wildlife Ecology, University of
Wisconsin-Madison, Madison, WI, USA
Huayi Wu
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Zhipeng Gui
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan, China
James T. Randerson
Department of Earth System Science, University of California Irvine, Irvine, CA, USA
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Cited
12 citations as recorded by crossref.
- Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks A. Masrur et al. 10.1016/j.ecoinf.2024.102760
- Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables Y. Ji et al. 10.3390/f15010216
- Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data Y. Liu et al. 10.5194/essd-16-3495-2024
- Trends and applications in wildfire burned area mapping: Remote sensing data, cloud geoprocessing platforms, and emerging algorithms D. Nelson et al. 10.1016/j.geomat.2024.100008
- Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion X. Guan et al. 10.3390/ijgi12040174
- 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
- Harnessing deep learning for forecasting fire-burning locations and unveiling $$PM_{2.5}$$ emissions S. Gaikwad et al. 10.1007/s40808-023-01831-1
- Research on fire accident prediction and risk assessment algorithm based on data mining and machine learning Z. Zhang et al. 10.1186/s13662-024-03845-0
- Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas Y. Yuan & A. Wylie 10.3390/ijgi13050149
- Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity K. Yuan et al. 10.1038/s41558-024-01933-3
- Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt R. Mahmoud et al. 10.3390/su15129467
- The role of terrain-mediated hydroclimate in vegetation recovery after wildfire R. Webb et al. 10.1088/1748-9326/acd803
12 citations as recorded by crossref.
- Capturing and interpreting wildfire spread dynamics: attention-based spatiotemporal models using ConvLSTM networks A. Masrur et al. 10.1016/j.ecoinf.2024.102760
- Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables Y. Ji et al. 10.3390/f15010216
- Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data Y. Liu et al. 10.5194/essd-16-3495-2024
- Trends and applications in wildfire burned area mapping: Remote sensing data, cloud geoprocessing platforms, and emerging algorithms D. Nelson et al. 10.1016/j.geomat.2024.100008
- Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion X. Guan et al. 10.3390/ijgi12040174
- 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
- Harnessing deep learning for forecasting fire-burning locations and unveiling $$PM_{2.5}$$ emissions S. Gaikwad et al. 10.1007/s40808-023-01831-1
- Research on fire accident prediction and risk assessment algorithm based on data mining and machine learning Z. Zhang et al. 10.1186/s13662-024-03845-0
- Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas Y. Yuan & A. Wylie 10.3390/ijgi13050149
- Boreal–Arctic wetland methane emissions modulated by warming and vegetation activity K. Yuan et al. 10.1038/s41558-024-01933-3
- Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt R. Mahmoud et al. 10.3390/su15129467
- The role of terrain-mediated hydroclimate in vegetation recovery after wildfire R. Webb et al. 10.1088/1748-9326/acd803
Latest update: 23 Nov 2024
Short summary
We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
We developed an interpretable machine learning model to predict sub-seasonal and near-future...
