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

Data sets

Data to reproduce the results Benedikt Heudorfer et al. https://github.com/KITHydrogeology/2023-global-model-germany

KITHydrogeology/2023-global-model-germany: v1 (Version v1) Benedikt Heudorfer et al. https://doi.org/10.5281/zenodo.10628600

Model code and software

Code to reproduce results Benedikt Heudorfer and Tanja Liesch https://github.com/KITHydrogeology/2023-global-model-germany

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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.