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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Cited articles

Abatzoglou, J. T. and Kolden, C. A.: Relationships between climate and macroscale area burned in the western United States, Int. J. Wildland Fire, 22, 1003–1020, 2013. 
Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, 2010. 
Amatulli, G., Rodrigues, M. J., Trombetti, M., and Lovreglio, R.: Assessing long-term fire risk at local scale by means of decision tree technique, J. Geophys. Res.-Biogeo., 111, G04S05, https://doi.org/10.1029/2005JG000133, 2006. 
Andela, N. and Van Der Werf, G. R.: Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition, Nat. Clim. Change, 4, 791–795, 2014. 
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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.