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


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
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.