Emulation of environmental models using polynomial chaos expansion. (January 2019)

• Record Type: Journal Article
• Authors: Massoud, Elias C.
• Is Part Of: Environmental modelling & software. Volume 111(2019)
• Page Start: 421
• Page End: 431
• Publication Date: 2019-01
• Subjects: Model emulation -- Surrogate modeling -- Polynomial chaos expansion -- Sensitivity analysis; 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.2018.10.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This paper investigates the applicability of model emulation to speed up simulation time of CPU intensive environmental models. Polynomial chaos expansion (PCE) emulators are constructed for three case studies of increasing complexity. The level of emulator training and the order of polynomial necessary to sufficiently build accurate emulators for each model are investigated. Although the PCE emulators shown here do not approximate well the outputs of parameter rich models (80 + parameters), results demonstrate that the emulators mimic closely outputs of relatively simple, low dimensional, simulation models (15 parameters or less). Furthermore, the PCE emulators are tested with applications such as Global Sensitivity Analysis (GSA). Results illustrate the advantages and drawbacks of using classical PCE emulators for treating computational limitation of complex environmental models. Highlights: PCE emulators are constructed with sparse grid sampling for 3 environmental models. Accuracy, efficiency, and fidelity of emulators were tested. For models that have low to medium complexity, the emulators worked well. For a hyper-complex model with 87 parameters, the emulators were relatively weak. Dimensionality reduction of the hyper-complex model allowed for better emulation.