Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-841-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-841-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2025
Research article |
| 18 Feb 2025
| 18 Feb 2025
Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information?
Sivarama Krishna Reddy Chidepudi
CORRESPONDING AUTHOR
Univ Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
Nicolas Massei
Univ Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
Abderrahim Jardani
Univ Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
Bastien Dieppois
Centre for Agroecology, Water and Resilience, Coventry University, Coventry, UK
Abel Henriot
BRGM, 3 av. C. Guillemin, 45060 Orleans CEDEX 02, France
Matthieu Fournier
Univ Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France
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Short summary
This study explores how deep learning can improve our understanding of groundwater levels, using an approach that combines climate data and physical characteristics of aquifers. By focusing on different types of groundwater levels and employing techniques like clustering and wavelet transform, the study highlights the importance of targeting relevant information. This research not only advances groundwater simulation but also emphasizes the benefits of different modelling approaches.
This study explores how deep learning can improve our understanding of groundwater levels, using...
