Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4103-2025
https://doi.org/10.5194/gmd-18-4103-2025
Model description paper
 | 
04 Jul 2025
Model description paper |  | 04 Jul 2025

ELM2.1-XGBfire1.0: improving wildfire prediction by integrating a machine learning fire model in a land surface model

Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen

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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-2024-151', Anonymous Referee #1, 28 Sep 2024
  • RC2: 'Comment on gmd-2024-151', Matthew Kasoar, 29 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ye Liu on behalf of the Authors (30 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (18 Dec 2024) by Fiona O'Connor
RR by Matthew Kasoar (15 Jan 2025)
RR by Anonymous Referee #1 (10 Feb 2025)
ED: Publish subject to technical corrections (31 Mar 2025) by Fiona O'Connor
AR by Ye Liu on behalf of the Authors (31 Mar 2025)  Author's response   Manuscript 
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Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
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