Articles | Volume 26, issue 6
Research article
 | Highlight paper
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

Model code and software

neuralhydrology F. Kratzert

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.