Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 7
Articles | Volume 30, issue 7
https://doi.org/10.5194/hess-30-2135-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-2135-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 7
Research article
 | 
16 Apr 2026
Research article |  | 16 Apr 2026

Interpretable feature incorporation machine-learning framework for flood magnitude estimation

Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater

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Short summary
This study aims to improve prediction and understanding of extreme flood events in near-natural catchments across the United Kingdom. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.
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