Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5449-2022
https://doi.org/10.5194/hess-26-5449-2022
Research article
 | 
01 Nov 2022
Research article |  | 01 Nov 2022

Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States

Kieran M. R. Hunt, Gwyneth R. Matthews, Florian Pappenberger, and Christel Prudhomme

Viewed

Total article views: 4,551 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,928 1,546 77 4,551 55 54
  • HTML: 2,928
  • PDF: 1,546
  • XML: 77
  • Total: 4,551
  • BibTeX: 55
  • EndNote: 54
Views and downloads (calculated since 08 Feb 2022)
Cumulative views and downloads (calculated since 08 Feb 2022)

Viewed (geographical distribution)

Total article views: 4,551 (including HTML, PDF, and XML) Thereof 4,307 with geography defined and 244 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 12 Apr 2024
Download
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.