Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 9
Articles | Volume 28, issue 9
https://doi.org/10.5194/hess-28-2107-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2107-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 9
Research article
 | 
14 May 2024
Research article |  | 14 May 2024

Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach

Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin

Viewed

Total article views: 2,064 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,527 477 60 2,064 40 36 37
  • HTML: 1,527
  • PDF: 477
  • XML: 60
  • Total: 2,064
  • Supplement: 40
  • BibTeX: 36
  • EndNote: 37
Views and downloads (calculated since 03 Nov 2023)
Cumulative views and downloads (calculated since 03 Nov 2023)

Viewed (geographical distribution)

Total article views: 2,064 (including HTML, PDF, and XML) Thereof 1,975 with geography defined and 89 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jun 2024
Download
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Read more