Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-525-2024
https://doi.org/10.5194/hess-28-525-2024
Research article
 | 
08 Feb 2024
Research article |  | 08 Feb 2024

On the challenges of global entity-aware deep learning models for groundwater level prediction

Benedikt Heudorfer, Tanja Liesch, and Stefan Broda

Related authors

Disentangling coastal groundwater level dynamics in a global dataset
Annika Nolte, Ezra Haaf, Benedikt Heudorfer, Steffen Bender, and Jens Hartmann
Hydrol. Earth Syst. Sci., 28, 1215–1249, https://doi.org/10.5194/hess-28-1215-2024,https://doi.org/10.5194/hess-28-1215-2024, 2024
Short summary
Evaluating integrated water management strategies to inform hydrological drought mitigation
Doris E. Wendt, John P. Bloomfield, Anne F. Van Loon, Margaret Garcia, Benedikt Heudorfer, Joshua Larsen, and David M. Hannah
Nat. Hazards Earth Syst. Sci., 21, 3113–3139, https://doi.org/10.5194/nhess-21-3113-2021,https://doi.org/10.5194/nhess-21-3113-2021, 2021
Short summary
The Groundwater Drought Initiative (GDI): Analysing and understanding groundwater drought across Europe
Bentje Brauns, Daniela Cuba, John P. Bloomfield, David M. Hannah, Christopher Jackson, Ben P. Marchant, Benedikt Heudorfer, Anne F. Van Loon, Hélène Bessière, Bo Thunholm, and Gerhard Schubert
Proc. IAHS, 383, 297–305, https://doi.org/10.5194/piahs-383-297-2020,https://doi.org/10.5194/piahs-383-297-2020, 2020
Short summary
The European 2015 drought from a hydrological perspective
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017,https://doi.org/10.5194/hess-21-3001-2017, 2017
Short summary

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Modelling approaches
Technical note: A model of chemical transport in a wellbore–aquifer system
Yiqun Gan and Quanrong Wang
Hydrol. Earth Syst. Sci., 28, 1317–1323, https://doi.org/10.5194/hess-28-1317-2024,https://doi.org/10.5194/hess-28-1317-2024, 2024
Short summary
Disentangling coastal groundwater level dynamics in a global dataset
Annika Nolte, Ezra Haaf, Benedikt Heudorfer, Steffen Bender, and Jens Hartmann
Hydrol. Earth Syst. Sci., 28, 1215–1249, https://doi.org/10.5194/hess-28-1215-2024,https://doi.org/10.5194/hess-28-1215-2024, 2024
Short summary
Current and future roles of meltwater–groundwater dynamics in a proglacial Alpine outwash plain
Tom Müller, Matteo Roncoroni, Davide Mancini, Stuart N. Lane, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 28, 735–759, https://doi.org/10.5194/hess-28-735-2024,https://doi.org/10.5194/hess-28-735-2024, 2024
Short summary
Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models
Trine Enemark, Rasmus Bødker Madsen, Torben O. Sonnenborg, Lærke Therese Andersen, Peter B. E. Sandersen, Jacob Kidmose, Ingelise Møller, Thomas Mejer Hansen, Karsten Høgh Jensen, and Anne-Sophie Høyer
Hydrol. Earth Syst. Sci., 28, 505–523, https://doi.org/10.5194/hess-28-505-2024,https://doi.org/10.5194/hess-28-505-2024, 2024
Short summary
Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions
Yong Zhang, Graham E. Fogg, HongGuang Sun, Donald M. Reeves, Roseanna M. Neupauer, and Wei Wei
Hydrol. Earth Syst. Sci., 28, 179–203, https://doi.org/10.5194/hess-28-179-2024,https://doi.org/10.5194/hess-28-179-2024, 2024
Short summary

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., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: Tensorflow: A system for large-scale machine learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2–4 November 2016, Savannah, USA, 265–283, 2016. a
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
Ahamed, A., Knight, R., Alam, S., Pauloo, R., and Melton, F.: Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage, Sci. Total Environ., 807, 150635, https://doi.org/10.1016/j.scitotenv.2021.150635, 2022. a
Barthel, R.: HESS Opinions ”Integration of groundwater and surface water research: an interdisciplinary problem?”, Hydrol. Earth Syst. Sci., 18, 2615–2628, https://doi.org/10.5194/hess-18-2615-2014, 2014. a
Bedi, S., Samal, A., Ray, C., and Snow, D.: Comparative evaluation of machine learning models for groundwater quality assessment, Environ. Monitor. A., 192, 1–23, 2020. a
Download
Short summary
We build a neural network to predict groundwater levels from monitoring wells. We predict all wells at the same time, by learning the differences between wells with static features, making it an entity-aware global model. This works, but we also test different static features and find that the model does not use them to learn exactly how the wells are different, but only to uniquely identify them. As this model class is not actually entity aware, we suggest further steps to make it so.