Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 5
Articles | Volume 28, issue 5
https://doi.org/10.5194/hess-28-1191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1191-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 5
Research article
 | 
13 Mar 2024
Research article |  | 13 Mar 2024

Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia

Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch

Viewed

Total article views: 1,972 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,465 450 57 1,972 47 50
  • HTML: 1,465
  • PDF: 450
  • XML: 57
  • Total: 1,972
  • BibTeX: 47
  • EndNote: 50
Views and downloads (calculated since 30 May 2023)
Cumulative views and downloads (calculated since 30 May 2023)

Viewed (geographical distribution)

Total article views: 1,972 (including HTML, PDF, and XML) Thereof 1,898 with geography defined and 74 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Nov 2024
Download
Short summary
To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Read more