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Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
Department Water Resources and Drinking Water (W+T), Eawag, Duebendorf, Switzerland.
Chalmers tekniska högskola.
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands.
Institute of Applied Geosciences, Division of Hydrogeology, Karlsruhe Institute of Technology, Karlsruhe, Germany.
2024 (English)In: Hydrology and Earth System Sciences, ISSN 1027-5606, E-ISSN 1607-7938, Vol. 28, no 23, p. 5193-5208Article in journal (Other academic) Published
Abstract [sv]

Denna artikel presenterar resultaten från 2022 års Groundwater Time Series Modelling Challenge, där 15 team från olika forskningsinstitut tillämpade olika datadrivna modeller för att simulera tidsserier för grundvattennivåer vid fyra övervakningsbrunnar.

Abstract [en]

This paper presents the results of the 2022 Groundwater Time Series Modelling Challenge, where 15 teams from different institutes applied various data-driven models to simulate hydraulic-head time series at four monitoring wells.

Place, publisher, year, edition, pages
Journal of Hydrology and Earth System Sciences, European Geosciences Union , 2024. Vol. 28, no 23, p. 5193-5208
Keywords [sv]
Bygga, grundvatten, grundvattensignaturer, hydrogeologi
National Category
Water Engineering
Research subject
FOI-portföljer; FOI-portföljer, Bygga
Identifiers
URN: urn:nbn:se:trafikverket:diva-22145DOI: 10.5194/hess-28-5193-2024Archive number: TRV 2019/45670OAI: oai:DiVA.org:trafikverket-22145DiVA, id: diva2:2056147
Projects
När var hur? – Identifikation av orsaker till hydrogeologiska störningar i undermarksbebyggelse
Funder
Swedish Transport Administration, TRV 2019/45670
Note

Lista över samtliga artikelförfattare:

Raoul A. Collenteur 

Ezra Haaf, Chalmers University of Technology

Mark Bakker, Delft University of Technology

Tanja Liesch, Karlsruhe Institute of Technology

Andreas Wunsch, Fraunhofer Institute of Optronics

Jenny Soonthornrangsan, Delft University of Technology

Jeremy White, Intera, Fort Collins

Nick Martin, Southwest Research Institute (SWRI)

Rui Hugman, Intera, Fort Collins

Ed de Sousa, Intera, Fort Collins

Didier Vanden Berghe, Burgeap, Ginger Group

Xinyang Fan, Friedrich-Alexander-UniversityErlangen-Nuremberg och University of Bern

Tim J. Peterson, Monash University

Janis Bikše, University of Latvia

Antoine Di Ciacca,  Lincoln Agritech Ltd

Xinyue Wang, Brown University

Yang Zheng, Brown University

Maximilian Nölscher, German Federal Institute for Geoscience and Resource

Julian Koch, Geological Survey of Denmark and Greenland

Raphael Schneider, Geological Survey of Denmark and Greenland

Nikolas Benavides Höglund, Lund University

Sivarama Krishna Reddy Chidepudi, Univ. Rouen Normandie och BRGM

Abel Henriot, BRGM

Nicolas Massei, Univ. Rouen Normandie

Abderrahim Jardani, Univ. Rouen Normandie

Max Gustav Rudolph,  Dresden University of Technology

Amir Rouhani, Helmholtz Centre forEnvironmental Research

J. Jaime Gómez-Hernández, Universitat Politècnica de València

Seifeddine Jomaa,  Helmholtz Centre forEnvironmental Research

Anna Pölz, TU Wien och Interuniversity Cooperation Centre Water and Health

Tim Franken, Sumaqua, Louvain

Morteza Behbooei, University of Waterloo

Jimmy Lin, University of Waterloo

Rojin Meysami, University of Waterloo

Available from: 2026-04-28 Created: 2026-04-28 Last updated: 2026-04-29

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Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge(2659 kB)12 downloads
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