Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 7
Articles | Volume 30, issue 7
https://doi.org/10.5194/hess-30-1877-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1877-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 7
Research article
 | 
09 Apr 2026
Research article |  | 09 Apr 2026

Strategies for incorporating static features into global deep learning models

Tanja Liesch and Marc Ohmer

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Cited articles

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zhen, X.: Tensorflow: A system for large-scale machine learning, in: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), 265–283, 2016. a
Arsenault, R., Martel, J.-L., Brunet, F., Brissette, F., and Mai, J.: Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models, Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, 2023. a
Chollet, F. and contributors: Keras, https://github.com/fchollet/keras (last access: 7 April 2026), 2015. a
Clark, S. R., Pagendam, D., and Ryan, L.: Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks, Int. J. Env. Res. Pub. He., 19, 5091, https://doi.org/10.3390/ijerph19095091, 2022. a
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We studied how to add site information to deep learning models that predict groundwater levels at many wells at once. Using data from Germany, we compared four simple ways to combine time varying weather with time invariant site characteristics. All methods gave similar average accuracy. Repeating site data at each time step was slightly best but used more computer power. The informativeness of site information mattered more than the method, guiding future model design.
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