Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 19
Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5131-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-5131-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 19
Research article
 | 
14 Oct 2025
Research article |  | 14 Oct 2025

Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany

Max Weißenborn, Lutz Breuer, and Tobias Houska

Viewed

Total article views: 5,781 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,664 1,044 1,073 5,781 103 118
  • HTML: 3,664
  • PDF: 1,044
  • XML: 1,073
  • Total: 5,781
  • BibTeX: 103
  • EndNote: 118
Views and downloads (calculated since 18 Jul 2024)
Cumulative views and downloads (calculated since 18 Jul 2024)

Viewed (geographical distribution)

Total article views: 5,781 (including HTML, PDF, and XML) Thereof 5,621 with geography defined and 160 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 30 Apr 2026
Download
Short summary
Our study compares neural network models for predicting discharge in ungauged basins. We evaluated convolutional neural networks (CNNs), long short-term memory (LSTM) and gated recurrent units (GRUs) using 28 years of weather data. CNNs showed the best accuracy, while GRUs were faster and nearly as accurate. Adding static features improved all models. The research enhances flood forecasting and water management in regions lacking direct measurements, offering efficient and accurate predictive tools.
Read more
Share