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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-195', Anonymous Referee #1, 25 Aug 2022
    • AC1: 'Reply on RC1', Qing Zhu, 20 Dec 2022
  • RC2: 'Comment on gmd-2022-195', Anonymous Referee #2, 22 Nov 2022
    • AC2: 'Reply on RC2', Qing Zhu, 20 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Qing Zhu on behalf of the Authors (20 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (29 Dec 2022) by Mohamed Salim
RR by Anonymous Referee #2 (05 Jan 2023)
RR by Anonymous Referee #1 (06 Jan 2023)
ED: Publish subject to technical corrections (07 Jan 2023) by Mohamed Salim
AR by Qing Zhu on behalf of the Authors (10 Jan 2023)  Author's response    Manuscript
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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.