Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 5
Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2951-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2951-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 5
Research article
 | 
31 May 2021
Research article |  | 31 May 2021

Machine-learning methods for stream water temperature prediction

Moritz Feigl, Katharina Lebiedzinski, Mathew Herrnegger, and Karsten Schulz

Viewed

Total article views: 8,121 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,640 2,387 94 8,121 152 181 200
  • HTML: 5,640
  • PDF: 2,387
  • XML: 94
  • Total: 8,121
  • Supplement: 152
  • BibTeX: 181
  • EndNote: 200
Views and downloads (calculated since 14 Jan 2021)
Cumulative views and downloads (calculated since 14 Jan 2021)

Viewed (geographical distribution)

Total article views: 8,121 (including HTML, PDF, and XML) Thereof 7,562 with geography defined and 559 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Nov 2025
Download
Short summary
In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.
Read more
Share