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Multivariate Sensitivity Analysis to Measure Global Contribution of Input Factors in Dynamic Models by Matieyendou Lamboni; Hervé Monod; David Makowski is a Engineering article available to read on EtoBox.

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Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 [1]) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the f

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Author

Matieyendou Lamboni; Hervé Monod; David Makowski

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0951-8320)

Published

2011

Language

EN

Field

Engineering (Physical Sciences)