Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 4
Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-945-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-945-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 4
Research article
 | 
27 Feb 2024
Research article |  | 27 Feb 2024

Toward interpretable LSTM-based modeling of hydrological systems

Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon

Viewed

Total article views: 5,171 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,030 1,054 87 5,171 266 96 113
  • HTML: 4,030
  • PDF: 1,054
  • XML: 87
  • Total: 5,171
  • Supplement: 266
  • BibTeX: 96
  • EndNote: 113
Views and downloads (calculated since 24 Oct 2023)
Cumulative views and downloads (calculated since 24 Oct 2023)

Viewed (geographical distribution)

Total article views: 5,171 (including HTML, PDF, and XML) Thereof 5,010 with geography defined and 161 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Dec 2025
Download
Short summary
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Read more
Share