Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-1961-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1961-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 May 2023
Research article |
| 23 May 2023
| 23 May 2023
Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions
Guillaume Cinkus
CORRESPONDING AUTHOR
HydroSciences Montpellier (HSM), Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
Andreas Wunsch
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Kaiserstr. 12, 76131 Karlsruhe, Germany
Naomi Mazzilli
UMR 1114 EMMAH (AU-INRAE), Université d'Avignon, 84000 Avignon, France
Tanja Liesch
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Kaiserstr. 12, 76131 Karlsruhe, Germany
Zhao Chen
Institute of Groundwater Management, Technical University of Dresden, 01062 Dresden, Germany
Nataša Ravbar
ZRC SAZU, Karst Research Institute, Titov trg 2, 6230 Postojna, Slovenia
Joanna Doummar
Department of Geology, American University of Beirut, P.O. Box 11 – 0236/26, Beirut, Lebanon
Jaime Fernández-Ortega
Department of Geology and Centre of Hydrogeology, University of Málaga (CEHIUMA), 29071 Málaga, Spain
Juan Antonio Barberá
Department of Geology and Centre of Hydrogeology, University of Málaga (CEHIUMA), 29071 Málaga, Spain
Bartolomé Andreo
Department of Geology and Centre of Hydrogeology, University of Málaga (CEHIUMA), 29071 Málaga, Spain
Nico Goldscheider
Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Kaiserstr. 12, 76131 Karlsruhe, Germany
Hervé Jourde
HydroSciences Montpellier (HSM), Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
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Cited
12 citations as recorded by crossref.
- Managing climate change impacts on the Western Mountain Aquifer: Implications for Mediterranean karst groundwater resources L. Bresinsky et al. 10.1016/j.hydroa.2023.100153
- A Fully Connected Neural Network (FCNN) Model to Simulate Karst Spring Flowrates in the Umbria Region (Central Italy) F. De Filippi et al. 10.3390/w16182580
- Performance of machine learning methods for modeling reservoir management based on irregular daily data sets: a case study of Zit Emba dam B. Lefoula et al. 10.1007/s12145-023-01160-y
- A common framework for the development of spring water contamination early warning system in western Mediterranean karst areas: Spanish and French sites J. Fernández-Ortega et al. 10.1016/j.scitotenv.2024.177294
- Influence of the karst matrix hydraulic conductivity and specific yield on the estimation accuracy of karstic water storage variation Y. Li et al. 10.1016/j.jhydrol.2023.130186
- Evaluating climate change impacts on snow cover and karst spring discharge in a data-scarce region: a case study of Iran N. Zeydalinejad et al. 10.1007/s11600-024-01400-9
- Predicting the Function of the Dissolution Rate with Depth Using Drilling Data from Shallow Strata at Karst Sites X. Xie et al. 10.3390/su151411191
- Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al. 10.5194/hess-28-4239-2024
- When best is the enemy of good – critical evaluation of performance criteria in hydrological models G. Cinkus et al. 10.5194/hess-27-2397-2023
- Quantifying the historic and future response of karst spring discharge to climate variability and change at a snow-influenced temperate catchment in central Europe X. Fan et al. 10.1007/s10040-023-02703-9
- Effects of global and climate change on the freshwater-seawater interface movement in a Mediterranean karst aquifer of Mallorca Island D. Puigserver et al. 10.1016/j.scitotenv.2023.169246
- Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning N. Bouhafa et al. 10.2166/ws.2024.092
11 citations as recorded by crossref.
- Managing climate change impacts on the Western Mountain Aquifer: Implications for Mediterranean karst groundwater resources L. Bresinsky et al. 10.1016/j.hydroa.2023.100153
- A Fully Connected Neural Network (FCNN) Model to Simulate Karst Spring Flowrates in the Umbria Region (Central Italy) F. De Filippi et al. 10.3390/w16182580
- Performance of machine learning methods for modeling reservoir management based on irregular daily data sets: a case study of Zit Emba dam B. Lefoula et al. 10.1007/s12145-023-01160-y
- A common framework for the development of spring water contamination early warning system in western Mediterranean karst areas: Spanish and French sites J. Fernández-Ortega et al. 10.1016/j.scitotenv.2024.177294
- Influence of the karst matrix hydraulic conductivity and specific yield on the estimation accuracy of karstic water storage variation Y. Li et al. 10.1016/j.jhydrol.2023.130186
- Evaluating climate change impacts on snow cover and karst spring discharge in a data-scarce region: a case study of Iran N. Zeydalinejad et al. 10.1007/s11600-024-01400-9
- Predicting the Function of the Dissolution Rate with Depth Using Drilling Data from Shallow Strata at Karst Sites X. Xie et al. 10.3390/su151411191
- Karst aquifer discharge response to rainfall interpreted as anomalous transport D. Elhanati et al. 10.5194/hess-28-4239-2024
- When best is the enemy of good – critical evaluation of performance criteria in hydrological models G. Cinkus et al. 10.5194/hess-27-2397-2023
- Quantifying the historic and future response of karst spring discharge to climate variability and change at a snow-influenced temperate catchment in central Europe X. Fan et al. 10.1007/s10040-023-02703-9
- Effects of global and climate change on the freshwater-seawater interface movement in a Mediterranean karst aquifer of Mallorca Island D. Puigserver et al. 10.1016/j.scitotenv.2023.169246
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
Numerous modelling approaches can be used for studying karst water resources, which can make it difficult for a stakeholder or researcher to choose the appropriate method. We conduct a comparison of two widely used karst modelling approaches: artificial neural networks (ANNs) and reservoir models. Results show that ANN models are very flexible and seem great for reproducing high flows. Reservoir models can work with relatively short time series and seem to accurately reproduce low flows.
Numerous modelling approaches can be used for studying karst water resources, which can make it...
