Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 7
Articles | Volume 19, issue 7
https://doi.org/10.5194/gmd-19-2657-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2657-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 7
Methods for assessment of models
 | 
07 Apr 2026
Methods for assessment of models |  | 07 Apr 2026

Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features

Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda

Related authors

GEMS-GER: a machine learning benchmark dataset of long-term groundwater levels in Germany with meteorological forcings and site-specific environmental features
Marc Ohmer, Tanja Liesch, Bastian Habbel, Benedikt Heudorfer, Mariana Gomez, Patrick Clos, Maximilian Nölscher, and Stefan Broda
Earth Syst. Sci. Data, 18, 77–95, https://doi.org/10.5194/essd-18-77-2026,https://doi.org/10.5194/essd-18-77-2026, 2026
Short summary
Strategies for Incorporating Static Features into Global Deep Learning Models
Tanja Liesch and Marc Ohmer
EGUsphere, https://doi.org/10.5194/egusphere-2025-4048,https://doi.org/10.5194/egusphere-2025-4048, 2025
Short summary
Never Train a Deep Learning Model on a Single Well? Revisiting Training Strategies for Groundwater Level Prediction
Marc Ohmer and Tanja Liesch
EGUsphere, https://doi.org/10.5194/egusphere-2025-4055,https://doi.org/10.5194/egusphere-2025-4055, 2025
Short summary
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
Hydrol. Earth Syst. Sci., 29, 3405–3433, https://doi.org/10.5194/hess-29-3405-2025,https://doi.org/10.5194/hess-29-3405-2025, 2025
Short summary
Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
Raoul A. Collenteur, Ezra Haaf, Mark Bakker, Tanja Liesch, Andreas Wunsch, Jenny Soonthornrangsan, Jeremy White, Nick Martin, Rui Hugman, Ed de Sousa, Didier Vanden Berghe, Xinyang Fan, Tim J. Peterson, Jānis Bikše, Antoine Di Ciacca, Xinyue Wang, Yang Zheng, Maximilian Nölscher, Julian Koch, Raphael Schneider, Nikolas Benavides Höglund, Sivarama Krishna Reddy Chidepudi, Abel Henriot, Nicolas Massei, Abderrahim Jardani, Max Gustav Rudolph, Amir Rouhani, J. Jaime Gómez-Hernández, Seifeddine Jomaa, Anna Pölz, Tim Franken, Morteza Behbooei, Jimmy Lin, and Rojin Meysami
Hydrol. Earth Syst. Sci., 28, 5193–5208, https://doi.org/10.5194/hess-28-5193-2024,https://doi.org/10.5194/hess-28-5193-2024, 2024
Short summary
More articles (9)

Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, https://www.tensorflow.org/ (last access: 14 April 2025), 2015. a
Ahmadi, A., Olyaei, M., Heydari, Z., Emami, M., Zeynolabedin, A., Ghomlaghi, A., Daccache, A., Fogg, G. E., and Sadegh, M.: Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis, Water, 14, 949, https://doi.org/10.3390/w14060949, 2022. a, b
Arlot, S. and Celisse, A.: A survey of cross-validation procedures for model selection, Statistics Surveys, 4, 40–79, https://doi.org/10.1214/09-SS054, 2010. a, b, c
Bergmeir, C. and Benítez, J. M.: Forecaster performance evaluation with cross-validation and variants, in: 2011 11th International Conference on Intelligent Systems Design and Applications, IEEE, 849–854, https://doi.org/10.1109/ISDA.2011.6121763, 2011. a, b, c, d
Bergmeir, C. and Benítez, J. M.: On the use of cross-validation for time series predictor evaluation, Information Sciences, 191, 192–213, https://doi.org/10.1016/j.ins.2011.12.028, 2012. a, b, c, d, e, f, g, h, i, j
More articles (35)
Download
Short summary
With the growing use of machine learning for groundwater level (GWL) prediction, proper performance estimation is crucial. This study compares three validation strategies—blocked cross-validation (bl-CV), repeated out-of-sample (repOOS), and out-of-sample (OOS)—for 1D-CNN and LSTM models using meteorological inputs. Results show that bl-CV offers the most reliable performance estimates, while OOS is the most uncertain, highlighting the need for careful method selection.
Read more
Share