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.
| Originalsprog | Engelsk |
|---|---|
| Artikelnummer | 8357 |
| Tidsskrift | GEUS Bulletin |
| Vol/bind | 53 |
| Antal sider | 7 |
| ISSN | 2597-2162 |
| DOI | |
| Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:The authors would like to acknowledge 阀nnovation Fund Denmark for funding this project. The authors would also like to thank Robin Thi-baut and two anonymous reviewers for a constructive review process that improved the quality of this paper. And a special thanks to handling editor, Hyojin Kim.
Funding Information:
This work was funded by the Innovation Fund Denmark, project 9065-00212B.
Publisher Copyright:
© 2023, GEUS - Geological Survey of Denmark and Greenland. All rights reserved.