Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 7
Articles | Volume 30, issue 7
https://doi.org/10.5194/hess-30-1877-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1877-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 7
Research article
 | 
09 Apr 2026
Research article |  | 09 Apr 2026

Strategies for incorporating static features into global deep learning models

Tanja Liesch and Marc Ohmer

Viewed

Total article views: 622 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
385 200 37 622 271 257
  • HTML: 385
  • PDF: 200
  • XML: 37
  • Total: 622
  • BibTeX: 271
  • EndNote: 257
Views and downloads (calculated since 03 Dec 2025)
Cumulative views and downloads (calculated since 03 Dec 2025)

Viewed (geographical distribution)

Total article views: 622 (including HTML, PDF, and XML) Thereof 613 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Apr 2026
Download
Short summary
We studied how to add site information to deep learning models that predict groundwater levels at many wells at once. Using data from Germany, we compared four simple ways to combine time varying weather with time invariant site characteristics. All methods gave similar average accuracy. Repeating site data at each time step was slightly best but used more computer power. The informativeness of site information mattered more than the method, guiding future model design.
Read more
Share