Authors: Hongli Liu – University of Alberta; Martyn P. Clark, Shervan Gharari – University of Saskatchewan; Razi Sheikholeslami – Sharif University of Technology; Jim Freer, Wouter J. M. Knoben, Chris Marsh – University of Saskatchewan; Simon Michael Papalexiou – University of Calgary
Title: pyVISCOUS: An open-source tool for computationally frugal global sensitivity analysis
Abstract: Sensitivity analysis is used to increase our understanding of the evaluated model and ease model parameter estimation. VISCOUS (VarIance-based Sensitivity analysis using COpUlaS) is a given-data, computationally frugal variance-based global sensitivity analysis framework. Grounded in Copula theory, VISCOUS uses a Gaussian mixture copula model (GMCM) to describe the relationship between model inputs (e.g., the perturbations in the model parameters) and outputs (e.g., the model responses given a parameter perturbation), and computes the Sobol sensitivity indices. In this work, we make three contributions to improve the applicability of VISCOUS. First, we provide additional derivations of VISCOUS and didactic examples to make VISCOUS easier to understand and apply for general readers. Second, we evaluate VISCOUS using three types of Sobol functions and provide a cautionary note on using VISCOUS to approximate Sobol’ sensitivity indices for applications where model inputs are of similar importance. Third, we provide an open-source code of VISCOUS in Python, namely, pyVISCOUS.