Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4641-2020
© Author(s) 2020. 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-24-4641-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Sep 2020
Research article |
| 24 Sep 2020
| 24 Sep 2020
Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
Department of Geosciences, University of Oslo, Oslo, Norway
Thomas V. Schuler
Department of Geosciences, University of Oslo, Oslo, Norway
John F. Burkhart
Department of Geosciences, University of Oslo, Oslo, Norway
Morten Hjorth-Jensen
Department of Geosciences, University of Oslo, Oslo, Norway
Department of Physics and Astronomy, Michigan State University, Michigan, USA
Viewed
Total article views: 3,559 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Oct 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,480 | 973 | 106 | 3,559 | 118 | 128 |
- HTML: 2,480
- PDF: 973
- XML: 106
- Total: 3,559
- BibTeX: 118
- EndNote: 128
Total article views: 2,725 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Sep 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,009 | 633 | 83 | 2,725 | 80 | 92 |
- HTML: 2,009
- PDF: 633
- XML: 83
- Total: 2,725
- BibTeX: 80
- EndNote: 92
Total article views: 834 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 07 Oct 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 471 | 340 | 23 | 834 | 38 | 36 |
- HTML: 471
- PDF: 340
- XML: 23
- Total: 834
- BibTeX: 38
- EndNote: 36
Viewed (geographical distribution)
Total article views: 3,559 (including HTML, PDF, and XML)
Thereof 3,317 with geography defined
and 242 with unknown origin.
Total article views: 2,725 (including HTML, PDF, and XML)
Thereof 2,591 with geography defined
and 134 with unknown origin.
Total article views: 834 (including HTML, PDF, and XML)
Thereof 726 with geography defined
and 108 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
16 citations as recorded by crossref.
- Machine learning meta-models for fast parameter identification of the lattice discrete particle model Y. Lyu et al. 10.1007/s00466-023-02320-z
- Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning S. Huang et al. 10.1016/j.watres.2024.122191
- Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
- Enhancing root-zone soil moisture estimation using Richards' equation and dynamic surface soil moisture data X. Zha et al. 10.1016/j.agwat.2025.109460
- Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al. 10.5194/gmd-14-821-2021
- A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
- Root zone soil moisture estimation with Random Forest C. Carranza et al. 10.1016/j.jhydrol.2020.125840
- A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
- Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept F. Cappelli et al. 10.1007/s00477-022-02332-w
- Integrated model for the fast assessment of flood volume: Modelling – management, uncertainty and sensitivity analysis B. Szeląg et al. 10.1016/j.jhydrol.2023.129967
- A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
- Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
- TeaNet: An Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections P. Parasar et al. 10.3390/su17094230
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. 10.1016/j.jhydrol.2025.134082
- A review of deep learning and machine learning techniques for hydrological inflow forecasting S. Latif & A. Ahmed 10.1007/s10668-023-03131-1
- An Example of Augmenting Regional Sensitivity Analysis Using Machine Learning Software R. Spear et al. 10.1029/2019WR026379
14 citations as recorded by crossref.
- Machine learning meta-models for fast parameter identification of the lattice discrete particle model Y. Lyu et al. 10.1007/s00466-023-02320-z
- Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning S. Huang et al. 10.1016/j.watres.2024.122191
- Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions S. Huang et al. 10.1029/2022WR032183
- Enhancing root-zone soil moisture estimation using Richards' equation and dynamic surface soil moisture data X. Zha et al. 10.1016/j.agwat.2025.109460
- Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology J. Burkhart et al. 10.5194/gmd-14-821-2021
- A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
- Root zone soil moisture estimation with Random Forest C. Carranza et al. 10.1016/j.jhydrol.2020.125840
- A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
- Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept F. Cappelli et al. 10.1007/s00477-022-02332-w
- Integrated model for the fast assessment of flood volume: Modelling – management, uncertainty and sensitivity analysis B. Szeląg et al. 10.1016/j.jhydrol.2023.129967
- A Review on Snowmelt Models: Progress and Prospect G. Zhou et al. 10.3390/su132011485
- Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States K. Hunt et al. 10.5194/hess-26-5449-2022
- TeaNet: An Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections P. Parasar et al. 10.3390/su17094230
- A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning F. Zhu et al. 10.1016/j.jhydrol.2025.134082
2 citations as recorded by crossref.
Latest update: 08 Oct 2025
