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Journal Articles Stochastic Environmental Research and Risk Assessment Year : 2015

Development of a surrogate model and sensitivity analysis for spatio-temporal numerical simulators

Abstract

To evaluate the consequences on human health of radionuclide releases in the environment, numerical simulators are used to model the radionuclide atmospheric dispersion. These codes can be time consuming and depend on many uncertain variables related to radionuclide, release or weather conditions. These variables are of different kind: scalar, functional and qualitative. Given the uncertain parameters, code provides spatial maps of radionuclide concentration for various moments. The objective is to assess how these uncertainties can affect the code predictions and to perform a global sensitivity analysis of code in order to identify the most influential uncertain parameters. This sensitivity analysis often calls for the estimation of variance-based importance measures, called Sobol’ indices. To estimate these indices, we propose a global methodology combining several advanced statistical techniques which enable to deal with the various natures of the uncertain inputs and the high dimension of model outputs. First, a quantification of input uncertainties is made based on data analysis and expert judgment. Then, an initial realistic sampling design is generated and the corresponding code simulations are performed. Based on this sample, a proper orthogonal decomposition of the spatial output is used and the main decomposition coefficients are modeled with Gaussian process surrogate model. The obtained spatial metamodel is then used to compute spatial maps of Sobol’ indices, yielding the identification of global and local influence of each input variable and the detection of areas with interactions. The impact of uncertainty quantification step on the results is also evaluated.
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Dates and versions

cea-03190144 , version 1 (06-04-2021)

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Amandine Marrel, Nadia Perot, Clémentine Mottet. Development of a surrogate model and sensitivity analysis for spatio-temporal numerical simulators. Stochastic Environmental Research and Risk Assessment, 2015, 29 (3), pp.959-974. ⟨10.1007/s00477-014-0927-y⟩. ⟨cea-03190144⟩
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