Bayesian selection of hydro-morphodynamic models under computational time constraints. (July 2018)

• Record Type: Journal Article
• Authors: Mohammadi, Farid; Kopmann, Rebekka; Guthke, Anneli; Oladyshkin, Sergey; Nowak, Wolfgang
• Is Part Of: Advances in water resources. Volume 117(2018)
• Page Start: 53
• Page End: 64
• Publication Date: 2018-07
• Subjects: Hydro-morphodynamics -- Bayesian model selection -- Bayesian model evidence -- Reduced model -- Polynomial chaos expansion -- Surrogate-enabled model selection; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2018.05.007
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: Applicability of Bayesian model selection is limited by high computational costs. Bayesian model selection have been combined with model reduction. Introduced correction factor accounts for approximation error in the reduced model. Model selection incorporating the correction factor yields a reliable ranking. We demonstrate our proposed approach on a case study for the lower Rhine river. Abstract: A variety of empirical formulas to predict river bed evolution with hydro-morphodynamic river models exists. Modelers lack objective guidance of how to select the most appropriate one for a specific application. Such guidance can be provided by Bayesian model selection (BMS). Its applicability is however limited by high computational costs. To transfer it to computationally expensive river modeling tasks, we propose to combine BMS with model reduction based on arbitrary Polynomial Chaos Expansion. To account for approximation errors in the reduced models, we introduce a novel correction factor that yields a reliable model ranking even under strong computational time constraints. We demonstrate our proposed approach on a case study for a 10-km stretch of the lower Rhine river. The correction factor may shield us from misleading model ranking results. In our case, the correction factor was shown to increase the confidence in model selection.