Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 3
Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 3
Research article
 | 
08 Feb 2024
Research article |  | 08 Feb 2024

On the challenges of global entity-aware deep learning models for groundwater level prediction

Benedikt Heudorfer, Tanja Liesch, and Stefan Broda

Viewed

Total article views: 3,362 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,488 808 66 3,362 174 77 84
  • HTML: 2,488
  • PDF: 808
  • XML: 66
  • Total: 3,362
  • Supplement: 174
  • BibTeX: 77
  • EndNote: 84
Views and downloads (calculated since 30 Aug 2023)
Cumulative views and downloads (calculated since 30 Aug 2023)

Viewed (geographical distribution)

Total article views: 3,362 (including HTML, PDF, and XML) Thereof 3,312 with geography defined and 50 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2025
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
We build a neural network to predict groundwater levels from monitoring wells. We predict all wells at the same time, by learning the differences between wells with static features, making it an entity-aware global model. This works, but we also test different static features and find that the model does not use them to learn exactly how the wells are different, but only to uniquely identify them. As this model class is not actually entity aware, we suggest further steps to make it so.
Read more
Share