Articles | Volume 26, issue 6
https://doi.org/10.5194/hess-26-1673-2022
https://doi.org/10.5194/hess-26-1673-2022
Research article
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31 Mar 2022
Research article | Highlight paper |  | 31 Mar 2022

Uncertainty estimation with deep learning for rainfall–runoff modeling

Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing

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Latest update: 22 Apr 2024
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Short summary
This contribution evaluates distributional runoff predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.