Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark

Mathias Busk Dahl*, Troels Norvin Vilhelmsen, Trine Enemark, Thomas Mejer Hansen

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Abstract

Results from numerical simulations play a vital role in the decision process of everyday groundwa-ter 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.

OriginalsprogEngelsk
Artikelnummer8357
TidsskriftGEUS Bulletin
Vol/bind53
ISSN2597-2162
DOI
StatusUdgivet - 2023

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