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

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Cited articles

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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
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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.
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