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Autor(en): Erdal, Daniel
Xiao, Sinan
Nowak, Wolfgang
Cirpka, Olaf A.
Titel: Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces
Erscheinungsdatum: 2020
Dokumentart: Zeitschriftenartikel
Seiten: 1813-1830
Erschienen in: Stochastic environmental research and risk assessment 34 (2020), S. 1813-1830
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-131687
http://elib.uni-stuttgart.de/handle/11682/13168
http://dx.doi.org/10.18419/opus-13149
ISSN: 1436-3240
1436-3259
Zusammenfassung: Ensemble-based uncertainty quantification and global sensitivity analysis of environmental models requires generating large ensembles of parameter-sets. This can already be difficult when analyzing moderately complex models based on partial differential equations because many parameter combinations cause an implausible model behavior even though the individual parameters are within plausible ranges. In this work, we apply Gaussian Process Emulators (GPE) as surrogate models in a sampling scheme. In an active-training phase of the surrogate model, we target the behavioral boundary of the parameter space before sampling this behavioral part of the parameter space more evenly by passive sampling. Active learning increases the subsequent sampling efficiency, but its additional costs pay off only for a sufficiently large sample size. We exemplify our idea with a catchment-scale subsurface flow model with uncertain material properties, boundary conditions, and geometric descriptors of the geological structure. We then perform a global-sensitivity analysis of the resulting behavioral dataset using the active-subspace method, which requires approximating the local sensitivities of the target quantity with respect to all parameters at all sampled locations in parameter space. The Gaussian Process Emulator implicitly provides an analytical expression for this gradient, thus improving the accuracy of the active-subspace construction. When applying the GPE-based preselection, 70-90% of the samples were confirmed to be behavioral by running the full model, whereas only 0.5% of the samples were behavioral in standard Monte-Carlo sampling without preselection. The GPE method also provided local sensitivities at minimal additional costs.
Enthalten in den Sammlungen:02 Fakultät Bau- und Umweltingenieurwissenschaften

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