Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 31, issue 4
Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-535-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-535-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 31, issue 4
Research article
 | 
13 Nov 2024
Research article |  | 13 Nov 2024

Learning extreme vegetation response to climate drivers with recurrent neural networks

Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora

Viewed

Total article views: 1,410 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,063 297 50 1,410 40 34
  • HTML: 1,063
  • PDF: 297
  • XML: 50
  • Total: 1,410
  • BibTeX: 40
  • EndNote: 34
Views and downloads (calculated since 17 Oct 2023)
Cumulative views and downloads (calculated since 17 Oct 2023)

Viewed (geographical distribution)

Total article views: 1,410 (including HTML, PDF, and XML) Thereof 1,352 with geography defined and 58 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Mar 2025
Download
Short summary
We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
Read more
Share