Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 15
Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3405-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 15
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

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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.
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