Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5449-2022
© Author(s) 2022. 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-26-5449-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
Kieran M. R. Hunt
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, UK
National Centre for Atmospheric Sciences, University of Reading, Reading, UK
Gwyneth R. Matthews
Department of Meteorology, University of Reading, Reading, UK
European Centre for Medium-Range Weather Forecasts, Reading, UK
Florian Pappenberger
European Centre for Medium-Range Weather Forecasts, Reading, UK
Christel Prudhomme
European Centre for Medium-Range Weather Forecasts, Reading, UK
Department of Geography and Environment, Loughborough University, Loughborough, UK
UK Centre for Ecology and Hydrology, Wallingford, UK
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- A Machine-Learning Framework for Modeling and Predicting Monthly Streamflow Time Series H. Dastour & Q. Hassan 10.3390/hydrology10040095
- Data reformation – A novel data processing technique enhancing machine learning applicability for predicting streamflow extremes V. Tran et al. 10.1016/j.advwatres.2023.104569
- Monthly Streamflow Prediction of the Source Region of the Yellow River Based on Long Short-Term Memory Considering Different Lagged Months H. Chu et al. 10.3390/w16040593
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- Basin-Scale Streamflow Projections for Greater Pamba River Basin, India Integrating GCM Ensemble Modelling and Flow Accumulation-Weighted LULC Overlay in Deep Learning Environment A. Geetha Raveendran Nair et al. 10.3390/su151914148
- Combining Standard Artificial Intelligence Models, Pre-Processing Techniques, and Post-Processing Methods to Improve the Accuracy of Monthly Runoff Predictions in Karst-Area Watersheds C. Mo et al. 10.3390/app13010088
- Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
- Interpreting runoff forecasting of long short-term memory network: An investigation using the integrated gradient method on runoff data from the Han River Basin X. Jing et al. 10.1016/j.ejrh.2023.101549
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- Performance of LSTM over SWAT in Rainfall-Runoff Modeling in a Small, Forested Watershed: A Case Study of Cork Brook, RI S. Shrestha & S. Pradhanang 10.3390/w15234194
- Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system M. Vu et al. 10.1016/j.scitotenv.2023.165494
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Latest update: 24 Apr 2024
Short summary
In this study, we use three models to forecast river streamflow operationally for 13 months (September 2020 to October 2021) at 10 gauges in the western US. The first model is a state-of-the-art physics-based streamflow model (GloFAS). The second applies a bias-correction technique to GloFAS. The third is a type of neural network (an LSTM). We find that all three are capable of producing skilful forecasts but that the LSTM performs the best, with skilful 5 d forecasts at nine stations.
In this study, we use three models to forecast river streamflow operationally for 13 months...