A GUI platform for uncertainty quantification of complex dynamical models. (February 2016)

• Record Type: Journal Article
• Authors: Wang, Chen; Duan, Qingyun; Tong, Charles H.; Di, Zhenhua; Gong, Wei
• Is Part Of: Environmental modelling & software. Volume 76(2016:Feb.)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-02
• Subjects: Uncertainty Quantification -- Design of experiments -- Sensitivity analysis -- Surrogate modeling -- Parameter optimization -- UQ-PyL; 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.2015.11.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations. It is widely used in engineering and geophysics fields to assess and predict the likelihood of various outcomes. This paper describes a UQ platform called UQ-PyL (Uncertainty Quantification Python Laboratory), a flexible software platform designed to quantify uncertainty of complex dynamical models. UQ-PyL integrates different kinds of UQ methods, including experimental design, statistical analysis, sensitivity analysis, surrogate modeling and parameter optimization. It is written in Python language and runs on all common operating systems. UQ-PyL has a graphical user interface that allows users to enter commands via pull-down menus. It is equipped with a model driver generator that allows any computer model to be linked with the software. We illustrate the different functions of UQ-PyL by applying it to the uncertainty analysis of the Sacramento Soil Moisture Accounting Model. We will also demonstrate that UQ-PyL can be applied to a wide range of applications. Highlights: Uncertainty Quantification Python Laboratory (UQ-PyL) software platform is presented. The major steps in UQ analyses of complex dynamical models are explained. Various UQ-PyL functions are illustrated through a hydrological modeling application.