Articles | Volume 13, issue 9
https://doi.org/10.5194/hess-13-1619-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-13-1619-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
11 Sep 2009
Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin
G. A. Corzo
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
D. P. Solomatine
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Water Resources Section, Delft University of Technology, Delft, The Netherlands
Hidayat
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Centre for Limnology, Indonesian Institute of Sciences, Cibinong, Indonesia
M. de Wit
Deltares (Delft|Hydraulics), Rotterdamseweg 185, Delft, The Netherlands
M. Werner
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Deltares (Delft|Hydraulics), Rotterdamseweg 185, Delft, The Netherlands
S. Uhlenbrook
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Vrije Universiteit Amsterdam, Faculteit der Aardwetenschappen, Amsterdam, The Netherlands
R. K. Price
Department of Water Engineering and Hydroinformatics, UNESCO-IHE, Westvest 7, 2611 AX Delft, The Netherlands
Water Resources Section, Delft University of Technology, Delft, The Netherlands
Viewed
Total article views: 3,547 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 10 Feb 2009)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,396 | 2,008 | 143 | 3,547 | 145 | 110 |
- HTML: 1,396
- PDF: 2,008
- XML: 143
- Total: 3,547
- BibTeX: 145
- EndNote: 110
Total article views: 2,823 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 11 Sep 2009)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,116 | 1,585 | 122 | 2,823 | 130 | 101 |
- HTML: 1,116
- PDF: 1,585
- XML: 122
- Total: 2,823
- BibTeX: 130
- EndNote: 101
Total article views: 724 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013, article published on 10 Feb 2009)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 280 | 423 | 21 | 724 | 15 | 9 |
- HTML: 280
- PDF: 423
- XML: 21
- Total: 724
- BibTeX: 15
- EndNote: 9
Cited
36 citations as recorded by crossref.
- Identifying impacts of global climate teleconnection patterns on land water storage using machine learning I. Kalu et al. 10.1016/j.ejrh.2023.101346
- Improving estimation capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty H. Jeong et al. 10.1016/j.jhydrol.2024.130942
- A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches F. Alizadeh et al. 10.2166/wcc.2020.006
- CoolVox: Advanced 3D convolutional neural network models for predicting solar radiation on building facades J. Han et al. 10.1007/s12273-021-0837-0
- Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region V. Minaya et al. 10.1016/j.ecoinf.2016.12.002
- Prediction of Discharge in a Tidal River Using Artificial Neural Networks H. Hidayat et al. 10.1061/(ASCE)HE.1943-5584.0000970
- The Delft-FEWS flow forecasting system M. Werner et al. 10.1016/j.envsoft.2012.07.010
- Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective S. Razavi et al. 10.1002/hyp.14596
- Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2011.06.019
- Scenario-based prediction of short-term river stage–discharge process using wavelet-EEMD-based relevance vector machine K. Roushangar & F. Alizadeh 10.2166/hydro.2018.023
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- Incorporating non-uniformity and non-linearity of hydrologic and catchment characteristics in rainfall–runoff modeling using conceptual, data-driven, and hybrid techniques V. Vidyarthi & A. Jain 10.2166/hydro.2022.088
- Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model F. Karami & A. Dariane 10.1080/15715124.2016.1203331
- Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems J. Hunter et al. 10.5194/hess-22-2987-2018
- Review of hydrological modelling in the Australian Alps: from rainfall-runoff to physically based models N. Harvey et al. 10.1080/13241583.2024.2343453
- Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach . Farhad Alizadeh et al. 10.1134/S0097807820010066
- Hydrology of inland tropical lowlands: the Kapuas and Mahakam wetlands H. Hidayat et al. 10.5194/hess-21-2579-2017
- Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran M. H. Kashani et al. 10.1016/j.jhydrol.2016.06.028
- Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models B. Mekonnen et al. 10.1080/02626667.2014.935778
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
- A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network G. Humphrey et al. 10.1016/j.jhydrol.2016.06.026
- A systematic review of Muskingum flood routing techniques A. Salvati et al. 10.1080/02626667.2024.2324132
- Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
- Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China Z. Huo et al. 10.1016/j.jhydrol.2011.11.054
- Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China L. Liu et al. 10.1016/j.jhydrol.2017.08.032
- Fuzzy Conceptual Hydrological Model for Water Flow Prediction M. Turan & M. Yurdusev 10.1007/s11269-015-1183-8
- A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683
- Wavelet analyses of neural networks based river discharge decomposition L. Campozano et al. 10.1002/hyp.13726
- Improving capability of conceptual modeling of watershed rainfall–runoff using hybrid wavelet-extreme learning machine approach K. Roushangar et al. 10.2166/hydro.2017.011
- Multi-objective groundwater management strategy under uncertainties for sustainable control of saltwater intrusion: Solution for an island country in the South Pacific A. Lal & B. Datta 10.1016/j.jenvman.2018.12.054
- Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. 10.1002/hyp.9559
- River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009
- Development of a sequential Artificial Neural Network for predicting river water levels based on Brahmaputra and Ganges water levels M. Siddiquee & M. Hossain 10.1007/s00521-015-1871-6
34 citations as recorded by crossref.
- Identifying impacts of global climate teleconnection patterns on land water storage using machine learning I. Kalu et al. 10.1016/j.ejrh.2023.101346
- Improving estimation capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty H. Jeong et al. 10.1016/j.jhydrol.2024.130942
- A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches F. Alizadeh et al. 10.2166/wcc.2020.006
- CoolVox: Advanced 3D convolutional neural network models for predicting solar radiation on building facades J. Han et al. 10.1007/s12273-021-0837-0
- Data-driven techniques for modelling the gross primary production of the páramo vegetation using climate data: Application in the Ecuadorian Andean region V. Minaya et al. 10.1016/j.ecoinf.2016.12.002
- Prediction of Discharge in a Tidal River Using Artificial Neural Networks H. Hidayat et al. 10.1061/(ASCE)HE.1943-5584.0000970
- The Delft-FEWS flow forecasting system M. Werner et al. 10.1016/j.envsoft.2012.07.010
- Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective S. Razavi et al. 10.1002/hyp.14596
- Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2011.06.019
- Scenario-based prediction of short-term river stage–discharge process using wavelet-EEMD-based relevance vector machine K. Roushangar & F. Alizadeh 10.2166/hydro.2018.023
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- Incorporating non-uniformity and non-linearity of hydrologic and catchment characteristics in rainfall–runoff modeling using conceptual, data-driven, and hybrid techniques V. Vidyarthi & A. Jain 10.2166/hydro.2022.088
- Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model F. Karami & A. Dariane 10.1080/15715124.2016.1203331
- Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems J. Hunter et al. 10.5194/hess-22-2987-2018
- Review of hydrological modelling in the Australian Alps: from rainfall-runoff to physically based models N. Harvey et al. 10.1080/13241583.2024.2343453
- Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach . Farhad Alizadeh et al. 10.1134/S0097807820010066
- Hydrology of inland tropical lowlands: the Kapuas and Mahakam wetlands H. Hidayat et al. 10.5194/hess-21-2579-2017
- Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran M. H. Kashani et al. 10.1016/j.jhydrol.2016.06.028
- Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models B. Mekonnen et al. 10.1080/02626667.2014.935778
- Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review B. Yifru et al. 10.3390/su16041376
- A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network G. Humphrey et al. 10.1016/j.jhydrol.2016.06.026
- A systematic review of Muskingum flood routing techniques A. Salvati et al. 10.1080/02626667.2024.2324132
- Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
- Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China Z. Huo et al. 10.1016/j.jhydrol.2011.11.054
- Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China L. Liu et al. 10.1016/j.jhydrol.2017.08.032
- Fuzzy Conceptual Hydrological Model for Water Flow Prediction M. Turan & M. Yurdusev 10.1007/s11269-015-1183-8
- A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
- Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683
- Wavelet analyses of neural networks based river discharge decomposition L. Campozano et al. 10.1002/hyp.13726
- Improving capability of conceptual modeling of watershed rainfall–runoff using hybrid wavelet-extreme learning machine approach K. Roushangar et al. 10.2166/hydro.2017.011
- Multi-objective groundwater management strategy under uncertainties for sustainable control of saltwater intrusion: Solution for an island country in the South Pacific A. Lal & B. Datta 10.1016/j.jenvman.2018.12.054
- Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al. 10.1002/hyp.9559
2 citations as recorded by crossref.
- River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin M. Akhtar et al. 10.5194/hess-13-1607-2009
- Development of a sequential Artificial Neural Network for predicting river water levels based on Brahmaputra and Ganges water levels M. Siddiquee & M. Hossain 10.1007/s00521-015-1871-6
Saved (preprint)
Latest update: 05 Feb 2025
