Metamodel-assisted analysis of an integrated model composition: An example using linked surface water – groundwater models. (September 2018)

• Record Type: Journal Article
• Authors: Christelis, Vasileios; Hughes, Andrew G.
• Is Part Of: Environmental modelling & software. Volume 107(2018)
• Page Start: 298
• Page End: 306
• Publication Date: 2018-09
• Subjects: Integrated modelling -- Metamodels -- Kriging -- OpenMI -- Monte Carlo simulation -- 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.2018.05.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Integrated modelling is a promising approach to simulate processes operating within complex environmental systems. It is possible, however, that this integration may lead to computationally expensive compositions. In order to retain the process fidelity without loss of accuracy, the use of Kriging metamodels is proposed to perform Monte Carlo simulation and sensitivity analysis, in lieu of compositions developed using the model linking standard OpenMI. Results from the Monte Carlo simulation showed that the metamodels were in a good agreement with the original responses. However, metamodels provided a less accurate approximation of the original output distribution for the composition which involved a stronger non-linear behaviour. The fast runtimes of the metamodels allowed for increased computational budgets leading to an accurate screening of the important parameters for an Elementary Effects Test. Overall, Kriging metamodels provided significant computational savings without compromising the quality of the outcomes, even using small training data sets. Highlights: Distributed groundwater models were linked together with a river model via the OpenMI software platform. Kriging metamodels were applied to facilitate analysis with the integrated models. The overall computational savings were in the range of 70–90%. Monte Carlo simulations demonstrate that metamodels were in good agreement with the responses of the integrated models. Sensitivity Analysis using the metamodels accurately identified the important parameters.