Metamodeling and global sensitivity analysis for computer models with correlated inputs: A practical approach tested with a 3D light interception computer model. (June 2017)

• Record Type: Journal Article
• Authors: Gauchi, J.-P.; Bensadoun, A.; Colas, F.; Colbach, N.
• Is Part Of: Environmental modelling & software. Volume 92(2017)
• Page Start: 40
• Page End: 56
• Publication Date: 2017-06
• Subjects: Agroecology -- FlorSys -- Correlated inputs -- Metamodeling -- Partial least squares regression -- Polynomial chaos expansion -- Sensitivity indices; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.12.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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 a 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. Highlights: A practical method is proposed for analyzing computer models. It simultaneously leads to a sensitivity analysis and a metamodeling of an output. The computer model inputs can be correlated. This method is based on a truncated Polynomial Chaos Expansion and PLS Regression. It was applied to a computer model for predicting crop:weed competition for light.