Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 12
Articles | Volume 29, issue 12
https://doi.org/10.5194/hess-29-2521-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2521-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 12
Review article
 | 
17 Jun 2025
Review article |  | 17 Jun 2025

Machine learning in stream and river water temperature modeling: a review and metrics for evaluation

Claudia Rebecca Corona and Terri Sue Hogue

Viewed

Total article views: 5,089 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,065 748 276 5,089 167 109 155
  • HTML: 4,065
  • PDF: 748
  • XML: 276
  • Total: 5,089
  • Supplement: 167
  • BibTeX: 109
  • EndNote: 155
Views and downloads (calculated since 26 Aug 2024)
Cumulative views and downloads (calculated since 26 Aug 2024)

Viewed (geographical distribution)

Total article views: 5,089 (including HTML, PDF, and XML) Thereof 4,980 with geography defined and 109 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Dec 2025
Download
Short summary
Stream water temperature (SWT) is a key indicator of water quality with implications for public use and health, ecosystem function, and aquatic life survival. Advances in modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, machine learning (ML) has been used in SWT modeling, but questions remain about how the use of ML improves insight into SWT causes and effects. This work reviews ML-SWT modeling studies (and metrics) and considers future directions.
Read more
Share