Cluster-Based Hydrologic Prediction Using Genetic Algorithm-Trained Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 12, Issue 1
Abstract
Most hydrological processes are nonlinear in nature. Although there have been many successful applications of artificial neural networks (ANNs) to capture these nonlinear relationships, there are cases when ANNs have not been able to predict flow extremes (low and high flows) accurately. In a more general sense, ANNs have not performed well when data are clustered. The objective of this study is to demonstrate the influence of clustering on neural network performance by constructing a cluster-based conjunction model based on clustering, neural networks, and genetic algorithm (GA). The performance of the GA-trained cluster-based model is compared to that of the Bayesian regularization back-propagation algorithm, the Levenberg–Marquatrdt algorithm, and a regular GA-trained ANN model. The cluster-based neural network model was tested on (1) chaotic time series data (the Henon map); (2) cross-correlated monthly streamflow data. Results from the study indicate that the cluster-based neural network model offers a promising alternative to its conventional counterparts in mapping fragmented input–output relationships. From threshold analysis it is found that the cluster-based neural network model was effective, compared to its counterparts, in capturing the dynamics of high flows. Improvement in clustering accuracy is shown to improve the performance of the cluster-based neural network model.
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Acknowledgments
The writers acknowledge the financial support of the Natural Sciences and Engineering Research Council (NSERC) of Canada through its Discovery Grants Program and the University of Saskatchewan through the Departmental Scholarship Program.
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Information & Authors
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Published In
Journal of Hydrologic Engineering
Volume 12 • Issue 1 • January 2007
Pages: 52 - 62
Copyright
© 2007 ASCE.
History
Received: Sep 10, 2004
Accepted: Mar 20, 2006
Published online: Jan 1, 2007
Published in print: Jan 2007
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