Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 3
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.
https://doi.org/10.5194/gmd-16-869-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 3
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

Supplement

https://doi.org/10.5194/gmd-16-869-2023-supplement

Data sets

Global Fire Emissions Database J. T. Randerson, G. R. van der Werf, L. Giglio, G. J. Collatz, and P. S. Kasibhatla https://doi.org/10.3334/ORNLDAAC/1293

NCEP--DOE AMIP-II Reanalysis (R-2) (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html) Masao Kanamitsu, Wesley Ebisuzaki, Jack Woollen, Shi-Keng Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter https://doi.org/10.1175/BAMS-83-11-1631

Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6 (https://luh.umd.edu/data.shtml) George C. Hurtt, Louise Chini, Ritvik Sahajpal, et al. https://doi.org/10.5194/gmd-13-5425-2020

Global patterns of current and future road infrastructure (https://www.globio.info/download-grip-dataset) Johan R Meijer, Mark A. J. Huijbregts, Kees C. G. J. Schotten, and Aafke M. Schipper https://doi.org/10.1088/1748-9326/aabd42

Mapping the global distribution of livestock (https://www.fao.org/dad-is/en/) Timothy P. Robinson, G. R. William Wint, Giulia Conchedda, Thomas P. Van Boeckel, Valentina Ercoli, Elisa Palamara, Giuseppina Cinardi, Laura D'Aietti, Simon I. Hay, and Marius Gilbert https://doi.org/10.1371/journal.pone.0096084

Climate Indices: Monthly Atmospheric and Ocean Time Series NOAA https://psl.noaa.gov/data/climateindices/list/

Model code and software

AttentionFire (1.0) F. Li, Q. Zhu, W. J. Riley, L. Zhao, L. Xu, K. Yuan, M. Chen, H. Wu, Z. Gui, J. Gong, and J. T. Randerson https://doi.org/10.5281/zenodo.7416437

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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.
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