Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3405-2025
https://doi.org/10.5194/hess-29-3405-2025
Research article
 | 
01 Aug 2025
Research article |  | 01 Aug 2025

Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany

Stefan Kunz, Alexander Schulz, Maria Wetzel, Maximilian Nölscher, Teodor Chiaburu, Felix Biessmann, and Stefan Broda

Viewed

Total article views: 757 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
546 186 25 757 17 25
  • HTML: 546
  • PDF: 186
  • XML: 25
  • Total: 757
  • BibTeX: 17
  • EndNote: 25
Views and downloads (calculated since 19 Nov 2024)
Cumulative views and downloads (calculated since 19 Nov 2024)

Viewed (geographical distribution)

Total article views: 757 (including HTML, PDF, and XML) Thereof 745 with geography defined and 12 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Aug 2025
Download
Short summary
Accurate groundwater level predictions are crucial for sustainable management. This study applies two machine learning models – Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) and the Temporal Fusion Transformer (TFT) – to forecast seasonal groundwater levels for 5288 wells across Germany. N-HiTS outperformed TFT, with both models performing well in diverse hydrogeological settings, particularly in lowlands with distinct seasonal dynamics.
Share