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Article Dans Une Revue Environmental Modelling and Software Année : 2017

Metamodeling and global sensitivity analysis for computer models with correlated inputs: A practical approach tested with a 3D light interception computer model

Résumé

Models of biophysical processes are often time-consuming and their inputs are frequently correlated. This situation of non-independence between the inputs is always a challenge in view of simultaneously achieving a global sensitivity analysis of the model output and a metamodeling of this output. In this paper, a novel practical method is proposed for reaching this two-fold goal. It is based on a truncated Polynomial Chaos Expansion of the output whose coefficients are estimated by Partial Least Squares Regression. The method is applied to computer model for heterogeneous canopies in arable crops, aimed to predict crop:weed competition for light. We now have fast-running metamodels that simultaneously provide good approximations of the outputs of this computer model and a clear overview of its input influences thanks to new sensitivity indices.
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Dates et versions

hal-01604586 , version 1 (02-10-2017)

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Paternité - Partage selon les Conditions Initiales

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Jean-Pierre Gauchi, Arnaud Bensadoun, Floriane Colas, Nathalie Colbach. Metamodeling and global sensitivity analysis for computer models with correlated inputs: A practical approach tested with a 3D light interception computer model. Environmental Modelling and Software, 2017, 2017 (92), pp.40-56. ⟨10.1016/j.envsoft.2016.12.005⟩. ⟨hal-01604586⟩
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