Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-795-2022
https://doi.org/10.5194/hess-26-795-2022
Research article
 | 
14 Feb 2022
Research article |  | 14 Feb 2022

Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling

Sam Anderson and Valentina Radić

Viewed

Total article views: 4,429 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,124 1,231 74 4,429 174 51 59
  • HTML: 3,124
  • PDF: 1,231
  • XML: 74
  • Total: 4,429
  • Supplement: 174
  • BibTeX: 51
  • EndNote: 59
Views and downloads (calculated since 11 Mar 2021)
Cumulative views and downloads (calculated since 11 Mar 2021)

Viewed (geographical distribution)

Total article views: 4,429 (including HTML, PDF, and XML) Thereof 4,114 with geography defined and 315 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Dec 2024
Download
Short summary
We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.