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Copyright (c) 2023 Mathias Busk Dahl, Troels Norvin Vilhelmsen, Trine Enemark, Thomas Mejer Hansen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Results from numerical simulations play a vital role in the decision process of everyday groundwater management. However, these simulations can be time-consuming for large-scale investigations, and it can be necessary to apply approximate methods instead. This study investigates the abilities of a neural network to replicate simulated drawdown from groundwater abstraction in a numerical groundwater model of the Egebjerg catchment, Denmark. We follow a generalised methodology that uses the information within the deterministic numerical model to create a training set for the neural network to learn from and extend the method to work in a 3D Danish groundwater model case. We compare the abilities of the trained neural network with the results of conventional computations in terms of speed and accuracy and argue that this approach has the potential to improve decision support for decision-makers within groundwater management.
How to Cite
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Copyright (c) 2023 Mathias Busk Dahl, Troels Norvin Vilhelmsen, Trine Enemark, Thomas Mejer Hansen
This work is licensed under a Creative Commons Attribution 4.0 International License.
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An annual collection of articles submitted to GEUS Bulletin and published throughout 2023. Published online only. This issue is open until the end of 2023.
Cover photo: satellite image is from the European Space Agency’s Copernicus Sentinel data 2023, as featured in Karlsson et al. 2023: A data set of monthly [...]
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