Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 11
Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2505-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2505-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 11
Research article
 | 
13 Jun 2024
Research article |  | 13 Jun 2024

Metamorphic testing of machine learning and conceptual hydrologic models

Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen

Viewed

Total article views: 4,060 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,103 873 84 4,060 235 94 106
  • HTML: 3,103
  • PDF: 873
  • XML: 84
  • Total: 4,060
  • Supplement: 235
  • BibTeX: 94
  • EndNote: 106
Views and downloads (calculated since 26 Jul 2023)
Cumulative views and downloads (calculated since 26 Jul 2023)

Viewed (geographical distribution)

Total article views: 4,060 (including HTML, PDF, and XML) Thereof 4,000 with geography defined and 60 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Read more
Share