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-29-4761-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4761-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 19
Research article
 | 
30 Sep 2025
Research article |  | 30 Sep 2025

Can causal discovery lead to a more robust prediction model for runoff signatures?

Hossein Abbasizadeh, Petr Maca, Martin Hanel, Mads Troldborg, and Amir AghaKouchak

Viewed

Total article views: 1,363 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,034 234 95 1,363 150 35 70
  • HTML: 1,034
  • PDF: 234
  • XML: 95
  • Total: 1,363
  • Supplement: 150
  • BibTeX: 35
  • EndNote: 70
Views and downloads (calculated since 02 Oct 2024)
Cumulative views and downloads (calculated since 02 Oct 2024)

Viewed (geographical distribution)

Total article views: 1,363 (including HTML, PDF, and XML) Thereof 1,356 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Oct 2025
Download
Short summary
Here, we represented catchments as networks of variables connected by cause-and-effect relationships. By comparing the performance of statistical and machine learning methods with and without incorporating causal information to predict runoff properties, we showed that causal information can enhance models' robustness by reducing the accuracy drop between the training and testing phases, improving the model's interpretability, and mitigating overfitting issues, especially with small training samples.
Read more
Share