Multiobjective Water Quality Model Calibration Using a Hybrid Genetic Algorithm and Neural Network–Based Approach
Publication: Journal of Environmental Engineering
Volume 136, Issue 10
Abstract
This study aims at coupling a hybrid genetic algorithm (HGA) and a neural network (NN) model for the multiobjective calibration of surface water quality models. The HGA is formed as a robust optimization algorithm through combining a real-coded genetic algorithm with a local search method, i.e., the Nelder-Mead simplex method (NMS). The NN model is developed to approximate the input-output response relationship underlying a numerical water quality model, and is then incorporated into the HGA framework, which results in the HGA-NN approach. This approach has the advantage of evaluating the objective function of the calibration model in a more efficient way. The HGA-NN approach is tested in the calibration of a CE-QUAL-W2 model which is set up to simulate the hydrodynamic process and water quality conditions in Lake Maumelle in central Arkansas. It is found that the HGA-NN approach can improve the computational efficiency. However, it does not guarantee the finding of the parameter values with a low objective function value. An adaptive HGA-NN approach is then proposed to improve its performance. In this adaptive approach, both the water quality model and the NN model are incorporated into the HGA framework. They are executed adaptively to evaluate the objective function. The application results demonstrate that the adaptive approach can be applied to the calibration of water quality models.
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Acknowledgments
The writers would like to thank the Natural Science and Engineering Research Council of Canada for funding this research. This study was also supported by the Natural Science Foundation of China (Grant No. UNSPECIFIED50609008), the Natural Science Foundation of Beijing (Grant No. UNSPECIFIED8082022), and the Major State Basic Research Development Program of China (Grant Nos. UNSPECIFIED2005CB724200 and UNSPECIFIED2005CB724201). The writers are grateful to the editors and the anonymous reviewers for their insightful comments.
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Published In
Journal of Environmental Engineering
Volume 136 • Issue 10 • October 2010
Pages: 1020 - 1031
Copyright
© 2010 ASCE.
History
Received: Jul 5, 2009
Accepted: Feb 4, 2010
Published online: Feb 8, 2010
Published in print: Oct 2010
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