Articles | Volume 28, issue 19
https://doi.org/10.5194/hess-28-4407-2024
© Author(s) 2024. 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-28-4407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Oct 2024
Research article |
| 07 Oct 2024
| 07 Oct 2024
Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Institute of Groundwater Management, TU Dresden, Dresden, Germany
Maximilian Nölscher
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
Andreas Hartmann
Institute of Groundwater Management, TU Dresden, Dresden, Germany
Stefan Broda
Federal Institute for Geosciences and Natural Resources, Berlin, Germany
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Cited
16 citations as recorded by crossref.
- 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
- 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
- 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
- Groundwater depth prediction based on CNN-GRU-attention model H. Wei et al. https://doi.org/10.1007/s10661-026-14993-z
- Modeling hydrological functioning of karst aquifer systems in Slovenia using geomorphological features and random forest algorithm M. Janža et al. https://doi.org/10.1016/j.ejrh.2025.102774
- Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning J. Lee et al. https://doi.org/10.3390/environments12030094
- A hybrid TFN–LSTM model for groundwater level forecasting in Rhode Island, USA H. Chu et al. https://doi.org/10.1016/j.jenvman.2026.130474
- Bundesweite Entwicklung der Grundwasserstände seit 1991: Langzeittrends, Variabilität und Auswirkungen der jüngsten Trockenphase M. Wetzel et al. https://doi.org/10.1007/s00767-026-00616-4
- Soil moisture as a key predictor for regional groundwater levels: a deep learning study from Brandenburg, Germany M. Eckert & A. Rudolph https://doi.org/10.1088/3033-4942/ae4266
- Groundwater level forecasting in response to climate change scenarios in southwestern Saskatchewan using wavelet decomposition and artificial neural networks A. Okasha et al. https://doi.org/10.1016/j.gsd.2025.101550
- GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features M. Ohmer et al. https://doi.org/10.5194/essd-18-77-2026
- Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios W. Sun et al. https://doi.org/10.1016/j.gsd.2026.101591
- 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
- Predicting Groundwater Levels Using Advanced Deep Learning Models: A Case Study of Raipur, India S. Thakur & S. Karmakar https://doi.org/10.1007/s41403-025-00536-4
- Explainable deep learning-based simulation for evaluating climate-driven future groundwater level changes in South Korea J. Hwang & K. Lee https://doi.org/10.1016/j.gsd.2025.101541
- Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features F. Doll et al. https://doi.org/10.5194/gmd-19-2657-2026
16 citations as recorded by crossref.
- 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
- 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
- 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
- Groundwater depth prediction based on CNN-GRU-attention model H. Wei et al. https://doi.org/10.1007/s10661-026-14993-z
- Modeling hydrological functioning of karst aquifer systems in Slovenia using geomorphological features and random forest algorithm M. Janža et al. https://doi.org/10.1016/j.ejrh.2025.102774
- Web-Based Baseflow Estimation in SWAT Considering Spatiotemporal Recession Characteristics Using Machine Learning J. Lee et al. https://doi.org/10.3390/environments12030094
- A hybrid TFN–LSTM model for groundwater level forecasting in Rhode Island, USA H. Chu et al. https://doi.org/10.1016/j.jenvman.2026.130474
- Bundesweite Entwicklung der Grundwasserstände seit 1991: Langzeittrends, Variabilität und Auswirkungen der jüngsten Trockenphase M. Wetzel et al. https://doi.org/10.1007/s00767-026-00616-4
- Soil moisture as a key predictor for regional groundwater levels: a deep learning study from Brandenburg, Germany M. Eckert & A. Rudolph https://doi.org/10.1088/3033-4942/ae4266
- Groundwater level forecasting in response to climate change scenarios in southwestern Saskatchewan using wavelet decomposition and artificial neural networks A. Okasha et al. https://doi.org/10.1016/j.gsd.2025.101550
- GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features M. Ohmer et al. https://doi.org/10.5194/essd-18-77-2026
- Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios W. Sun et al. https://doi.org/10.1016/j.gsd.2026.101591
- 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
- Predicting Groundwater Levels Using Advanced Deep Learning Models: A Case Study of Raipur, India S. Thakur & S. Karmakar https://doi.org/10.1007/s41403-025-00536-4
- Explainable deep learning-based simulation for evaluating climate-driven future groundwater level changes in South Korea J. Hwang & K. Lee https://doi.org/10.1016/j.gsd.2025.101541
- Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features F. Doll et al. https://doi.org/10.5194/gmd-19-2657-2026
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
To understand the impact of external factors on groundwater level modelling using a 1-D convolutional neural network (CNN) model, we train, validate, and tune individual CNN models for 505 wells distributed across Lower Saxony, Germany. We then evaluate the performance of these models against available geospatial and time series features. This study provides new insights into the relationship between these factors and the accuracy of groundwater modelling.
To understand the impact of external factors on groundwater level modelling using a 1-D...
