dc.contributor.authorVrugt, Jasper A.
dc.contributor.authorÓ Nualláin, Breanndán
dc.contributor.authorRobinson, Bruce A.
dc.contributor.authorBouten, Willem
dc.contributor.authorDekker, Stefan C.
dc.contributor.authorSloot, Peter M. A.
dc.identifier.citationVrugt, J. A., Ó Nualláin, B., Robinson, B. A., Bouten, W., Dekker, S. C., & Sloot, P. M. A. (2005). Application of parallel computing to stochastic parameter estimation in environmental models. Computers & Geosciences, 32(8), 1139-1155.en_US
dc.description.abstractParameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems.en_US
dc.relation.ispartofseriesComputers & geosciencesen_US
dc.rights© 2005 Elsevier Ltd.en_US
dc.titleApplication of parallel computing to stochastic parameter estimation in environmental modelsen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US

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