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
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- 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
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Latest update: 29 Oct 2025
