Water Distribution System Optimization Using Metamodels
Publication: Journal of Water Resources Planning and Management
Volume 131, Issue 3
Abstract
Genetic algorithms (GAs) have been shown to apply well to optimizing the design and operations of water distribution systems (WDSs). The objective has usually been to minimize cost, subject to hydraulic constraints such as satisfying minimum pressure. More recently, the focus of optimization has expanded to include water quality concerns. This added complexity significantly increases the computational requirements of optimization. Considerable savings in computer time can be achieved by using a technique known as metamodeling. A metamodel is a surrogate or substitute for a complex simulation model. This research uses a metamodeling approach to optimize a water distribution design problem that includes water quality. The type of metamodels used are artificial neural networks (ANNs), as they are capable of approximating the nonlinear functions that govern flow and chlorine decay in a WDS. The ANNs were calibrated to provide a good approximation to the simulation model. In addition, two techniques are presented to improve the ability of metamodels to find the same optimal solution as the simulation model. Large savings in computer time occurred from training the ANNs to approximate chlorine concentrations (approximately 700 times faster than the simulation model) while still finding the optimal solution.
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Acknowledgments
The writers would like to thank the Co-operative Research Centre for Water Quality and Treatment, based in Adelaide, Australia, and the Australian Department of Education, Science and Training for their financial support of this project. The writers would also like to thank the three reviewers for their comments.
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Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 131 • Issue 3 • May 2005
Pages: 172 - 180
Copyright
© 2005 ASCE.
History
Received: Sep 2, 2004
Accepted: Dec 1, 2004
Published online: May 1, 2005
Published in print: May 2005
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