Uncertainty estimation with deep learning for rainfall–runoff modeling

Abstract Deep learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable hydrological prediction, and while standardized community benchmarks are becoming an increasingly important part of hydrological model development and research, similar tools for benchmarking uncertainty estimation are lacking. This contribution demonstrates that accurate uncertainty predictions can be obtained with deep learning. We establish an uncertainty estimation benchmarking procedure and present four deep learning baselines. Three baselines are based on mixture density networks, and one is based on Monte Carlo dropout. The results indicate that these approaches constitute strong baselines, especially the former ones. Additionally, we provide a post hoc model analysis to put forward some qualitative understanding of the resulting models. The analysis extends the notion of performance and shows that the model learns nuanced behaviors to account for different situations.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Uncertainty estimation with deep learning for rainfall–runoff modeling ; volume:26 ; number:6 ; year:2022 ; pages:1673-1693 ; extent:21 ; Hydrology and earth system sciences ; 26, Heft 6 (2022), 1673-1693 (gesamt 21)

Beteiligte, Orts- und Zeitangaben

• Urheber: Klotz, Daniel ; Kratzert, Frederik ; Gauch, Martin ; Keefe Sampson, Alden ; Brandstetter, Johannes ; Klambauer, Günter ; Hochreiter, Sepp ; Nearing, Grey

Weitere Informationen

• DOI: 10.5194/hess-26-1673-2022
• URN: urn:nbn:de:101:1-2022041210331002032205
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:24 MESZ