Articles | Volume 16, issue 3
https://doi.org/10.5194/gmd-16-869-2023
https://doi.org/10.5194/gmd-16-869-2023
Model description paper
 | 
03 Feb 2023
Model description paper |  | 03 Feb 2023

AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics

Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson

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Latest update: 17 Jul 2024
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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.