Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 13
Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4103-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4103-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 13
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

Viewed

Total article views: 3,785 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,935 481 369 3,785 84 147
  • HTML: 2,935
  • PDF: 481
  • XML: 369
  • Total: 3,785
  • BibTeX: 84
  • EndNote: 147
Views and downloads (calculated since 30 Aug 2024)
Cumulative views and downloads (calculated since 30 Aug 2024)

Viewed (geographical distribution)

Total article views: 3,785 (including HTML, PDF, and XML) Thereof 3,684 with geography defined and 101 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Jan 2026
Download
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.
Read more
Share