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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Cited
14 citations as recorded by crossref.
- Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
- Functional zonation of a semi-arid urban aquifer using unsupervised learning of dynamic groundwater behavior . Arifullah et al. https://doi.org/10.1016/j.envadv.2026.100695
- Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al. https://doi.org/10.5194/hess-29-3405-2025
- An ensemble learning framework for streamflow reconstruction incorporating aleatoric and epistemic uncertainty D. Ludyawati et al. https://doi.org/10.1016/j.ejrh.2026.103604
- Deep Learning and Transformer Models for Groundwater Level Prediction in the Marvdasht Plain: Protecting UNESCO Heritage Sites—Persepolis and Naqsh-e Rustam P. Heidarian et al. https://doi.org/10.3390/rs17142532
- A unified framework for groundwater level imputation and forecasting in data-limited catchments Y. Sharma et al. https://doi.org/10.1016/j.jhydrol.2026.135944
- Can deep learning outperform mechanistic modeling of peatland water table dynamics? H. Van Nieuwenhove et al. https://doi.org/10.1088/3049-4753/ae7726
- Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al. https://doi.org/10.2166/hydro.2025.095
- Interpretable Deep Learning for Characterizing Sinkhole to Supply Well Transfer Dynamics in Karst Aquifers B. Nigon et al. https://doi.org/10.3390/hydrology13040102
- Performance and Limitations of Machine Learning Models for Groundwater Level Prediction Using Hydro-Climatic Variables N. Zeydalinejad et al. https://doi.org/10.1007/s41748-026-01281-6
- A physically interpretable transfer learning framework for improved generalization of data-driven groundwater level prediction J. Jeong et al. https://doi.org/10.1016/j.watres.2026.126321
- Causally guided symbolic regression for basin-scale water balance change in the China–Mongolia arid region R. Li et al. https://doi.org/10.1016/j.ejrh.2026.103355
- From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta https://doi.org/10.1016/j.envsoft.2025.106696
- Deep learning for groundwater level simulation in unconfined aquifers across the contiguous United States: Analyzing simulations at multiple lead times and integrating groundwater signatures K. Boo et al. https://doi.org/10.1016/j.jhydrol.2026.134949
14 citations as recorded by crossref.
- Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction M. Ohmer & T. Liesch https://doi.org/10.5194/hess-30-2373-2026
- Functional zonation of a semi-arid urban aquifer using unsupervised learning of dynamic groundwater behavior . Arifullah et al. https://doi.org/10.1016/j.envadv.2026.100695
- Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany S. Kunz et al. https://doi.org/10.5194/hess-29-3405-2025
- An ensemble learning framework for streamflow reconstruction incorporating aleatoric and epistemic uncertainty D. Ludyawati et al. https://doi.org/10.1016/j.ejrh.2026.103604
- Deep Learning and Transformer Models for Groundwater Level Prediction in the Marvdasht Plain: Protecting UNESCO Heritage Sites—Persepolis and Naqsh-e Rustam P. Heidarian et al. https://doi.org/10.3390/rs17142532
- A unified framework for groundwater level imputation and forecasting in data-limited catchments Y. Sharma et al. https://doi.org/10.1016/j.jhydrol.2026.135944
- Can deep learning outperform mechanistic modeling of peatland water table dynamics? H. Van Nieuwenhove et al. https://doi.org/10.1088/3049-4753/ae7726
- Multi-site deep learning for groundwater level prediction across global datasets: toward scalable applications under data scarcity A. Nolte et al. https://doi.org/10.2166/hydro.2025.095
- Interpretable Deep Learning for Characterizing Sinkhole to Supply Well Transfer Dynamics in Karst Aquifers B. Nigon et al. https://doi.org/10.3390/hydrology13040102
- Performance and Limitations of Machine Learning Models for Groundwater Level Prediction Using Hydro-Climatic Variables N. Zeydalinejad et al. https://doi.org/10.1007/s41748-026-01281-6
- A physically interpretable transfer learning framework for improved generalization of data-driven groundwater level prediction J. Jeong et al. https://doi.org/10.1016/j.watres.2026.126321
- Causally guided symbolic regression for basin-scale water balance change in the China–Mongolia arid region R. Li et al. https://doi.org/10.1016/j.ejrh.2026.103355
- From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta https://doi.org/10.1016/j.envsoft.2025.106696
- Deep learning for groundwater level simulation in unconfined aquifers across the contiguous United States: Analyzing simulations at multiple lead times and integrating groundwater signatures K. Boo et al. https://doi.org/10.1016/j.jhydrol.2026.134949
Saved (final revised paper)
Latest update: 21 Jul 2026
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...
