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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark. / Dahl, Mathias Busk; Vilhelmsen, Troels Norvin; Enemark, Trine; Hansen, Thomas Mejer.

In: GEUS Bulletin, Vol. 53, 8357, 2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Dahl, MB, Vilhelmsen, TN, Enemark, T & Hansen, TM 2023, 'Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark', GEUS Bulletin, vol. 53, 8357. https://doi.org/10.34194/geusb.v53.8357

APA

Dahl, M. B., Vilhelmsen, T. N., Enemark, T., & Hansen, T. M. (2023). Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark. GEUS Bulletin, 53, [8357]. https://doi.org/10.34194/geusb.v53.8357

Vancouver

Dahl MB, Vilhelmsen TN, Enemark T, Hansen TM. Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark. GEUS Bulletin. 2023;53. 8357. https://doi.org/10.34194/geusb.v53.8357

Author

Dahl, Mathias Busk ; Vilhelmsen, Troels Norvin ; Enemark, Trine ; Hansen, Thomas Mejer. / Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark. In: GEUS Bulletin. 2023 ; Vol. 53.

Bibtex

@article{108747a21bf94e5897d6fcfad55d0f86,
title = "Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark",
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.",
keywords = "decision support, groundwater modelling, machine learning, probabilistic neural network, resource management",
author = "Dahl, {Mathias Busk} and Vilhelmsen, {Troels Norvin} and Trine Enemark and Hansen, {Thomas Mejer}",
note = "Publisher Copyright: {\textcopyright} 2023, GEUS - Geological Survey of Denmark and Greenland. All rights reserved.",
year = "2023",
doi = "10.34194/geusb.v53.8357",
language = "English",
volume = "53",
journal = "GEUS Bulletin",
issn = "2597-2162",
publisher = "Geological Survey of Denmark and Greenland (GEUS)",

}

RIS

TY - JOUR

T1 - Neural network predictions of drawdown from groundwater abstraction in the Egebjerg catchment, Denmark

AU - Dahl, Mathias Busk

AU - Vilhelmsen, Troels Norvin

AU - Enemark, Trine

AU - Hansen, Thomas Mejer

N1 - Publisher Copyright: © 2023, GEUS - Geological Survey of Denmark and Greenland. All rights reserved.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - decision support

KW - groundwater modelling

KW - machine learning

KW - probabilistic neural network

KW - resource management

U2 - 10.34194/geusb.v53.8357

DO - 10.34194/geusb.v53.8357

M3 - Journal article

AN - SCOPUS:85176910935

VL - 53

JO - GEUS Bulletin

JF - GEUS Bulletin

SN - 2597-2162

M1 - 8357

ER -

ID: 376451847