Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-4761-2025
https://doi.org/10.5194/hess-29-4761-2025
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

Data sets

CAMELS: Catchment Attributes and MEteorology for Large-sample Studies, Version 1.2 A. Newman et al. https://doi.org/10.5065/D6MW2F4D

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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.
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