Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-139-2023
© 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-27-139-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2023
Research article |
| 09 Jan 2023
| 09 Jan 2023
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Richard Arsenault
CORRESPONDING AUTHOR
Hydrology, Climate and Climate Change Laboratory, École de technologie supérieure, 1100 Notre-Dame West, Montréal, Québec H3C 1K3, Canada
Jean-Luc Martel
Hydrology, Climate and Climate Change Laboratory, École de technologie supérieure, 1100 Notre-Dame West, Montréal, Québec H3C 1K3, Canada
Frédéric Brunet
Hydrology, Climate and Climate Change Laboratory, École de technologie supérieure, 1100 Notre-Dame West, Montréal, Québec H3C 1K3, Canada
François Brissette
Hydrology, Climate and Climate Change Laboratory, École de technologie supérieure, 1100 Notre-Dame West, Montréal, Québec H3C 1K3, Canada
Juliane Mai
Department of Civil and Environmental Engineering, University of Waterloo, 200 University Ave W, Waterloo, Ontario N2L 3G1, Canada
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- Revisit hydrological modeling in ungauged catchments comparing regionalization, satellite observations, and machine learning approaches R. Dasgupta et al. 10.1016/j.hydres.2023.11.001
- Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
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- Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
- A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions N. Dong et al. 10.1016/j.jhydrol.2023.129246
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- Development of The Navigator: A Lagrangian sensing system to characterize surface freshwater ecosystems A. Khandelwal et al. 10.1016/j.watres.2023.120577
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma 10.1016/j.jhydrol.2024.130635
- 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
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al. 10.1016/j.jhydrol.2023.130380
- Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation G. Oh 10.53759/7669/jmc202404008
- Bayesian extreme learning machines for hydrological prediction uncertainty J. Quilty et al. 10.1016/j.jhydrol.2023.130138
- Satellite-based rainfall estimates to simulate daily streamflow using a hydrological model over Gambia watershed B. Faty et al. 10.1080/23570008.2023.2225898
- Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach S. Chen et al. 10.1016/j.jhydrol.2023.129734
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
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Latest update: 17 Apr 2024
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Predicting flow in rivers where no observation records are available is a daunting task. For...
