Sensitivity analysis in model calibration: GSA-GLUE approach Original Research Article

A new approach is presented applicable in framework of model calibration to observed data. The approach consists of a combination of the Generalized Likelihood Uncertainty Estimation technique (GLUE) and Global Sensitivity Analysis (GSA). The method is based on multiple model evaluations. The GSA is a quantitative, model independent approach and is based on estimating the fractional contribution of each input factor to the variance of the model output, also accounting for interaction terms. In GLUE, the model runs are classified according to a likelihood measure, conditioning each run to observations. In calibration procedures, strong interaction is observed between model parameters, due to model over-parameterization. The use of likelihood measures allows an estimate of the posterior joint pdf of parameters. By performing a GSA to the likelihood measure, input factors mainly driving model runs with good fit to data are identified. Moreover GSA allows highlighting the basic features of the interaction structure. Any other tool subsequently adopted to represent in more detail the interaction structure, from correlation coefficients to Principal Component Analysis to Bayesian networks to tree-structured density estimation, confirms the general features identified by GSA.