Biogeosciences
Biogeosciences
BG
Articles | Volume 22, issue 1
Articles | Volume 22, issue 1
https://doi.org/10.5194/bg-22-257-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-257-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 1
Research article
 | 
13 Jan 2025
Research article |  | 13 Jan 2025

Explainable machine learning for modeling of net ecosystem exchange in boreal forests

Ekaterina Ezhova, Topi Laanti, Anna Lintunen, Pasi Kolari, Tuomo Nieminen, Ivan Mammarella, Keijo Heljanko, and Markku Kulmala

Viewed

Total article views: 1,310 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
967 295 48 1,310 58 42
  • HTML: 967
  • PDF: 295
  • XML: 48
  • Total: 1,310
  • BibTeX: 58
  • EndNote: 42
Views and downloads (calculated since 06 Dec 2023)
Cumulative views and downloads (calculated since 06 Dec 2023)

Viewed (geographical distribution)

Total article views: 1,310 (including HTML, PDF, and XML) Thereof 1,282 with geography defined and 28 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
Short summary
Machine learning (ML) models are gaining popularity in biogeosciences. They are applied as gap-filling methods and used to upscale carbon fluxes to larger areas. Here we use explainable artificial intelligence (XAI) methods to elucidate the performance of machine learning models for carbon dioxide fluxes in boreal forests. We show that statistically equal models treat input variables differently. XAI methods can help scientists make informed decisions when applying ML models in their research.
Read more
Share
Altmetrics
Final-revised paper
Preprint