Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark
DOI:
https://doi.org/10.34194/geusb.v53.8357Keywords:
Decision support, Groundwater modelling, Machine learning, Probabilistic neural network, Resource managementAbstract
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.
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Copyright (c) 2023 Mathias Busk Dahl, Troels Norvin Vilhelmsen, Trine Enemark, Thomas Mejer Hansen
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GEUS Bulletin is an open-access, peer-reviewed journal published by the Geological Survey of Denmark and Greenland (GEUS). This article is distributed under a CC-BY 4.0 licence, permitting free redistribution and reproduction for any purpose, even commercial, provided proper citation of the original work. Author(s) retain copyright over the article contents. Read the full open access policy.
