Principal Factor Analysis for Forecasting Diurnal Water-Demand Pattern Using Combined Rough-Set and Fuzzy-Clustering Technique
Publication: Journal of Water Resources Planning and Management
Volume 139, Issue 1
Abstract
The true principal factors for the diurnal water-demand pattern of urban water are often difficult to identify using traditional rough-set algorithms because the demand pattern is usually affected by many factors that are uncertain and hard to quantify. An improved attribute-reduction algorithm based on the cumulative weighting coefficient was proposed to solve this problem. The weighting coefficient was determined by the result of the variable precision rough-set algorithm. To discuss the consecutive curves with mathematical tools, an improved fuzzy c-mean (FCM) algorithm was proposed to discretize the diurnal water-demand pattern spatially. The proposed algorithms were then used to analyze the principal factors of the diurnal water-demand pattern in the city of Hangzhou, China. The results show that the improved attribute-reduction algorithm is capable of distinguishing the false attribute from the dynamic reduction sets, and the fuzzy c-mean algorithm is an effective and feasible method of solving the cluster problem for the consecutive curves. The principal factors affecting the diurnal water-demand pattern in Hangzhou are maximum air temperature, minimum air temperature, and weekday or weekend.
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Acknowledgments
The authors would like to thank the editor and the anonymous reviewers for the helpful comments on the paper. This work was supported by the key Special Program on the S&T of China for the Pollution Control and Treatment of Water Bodies (2009ZX07421-005, 2009ZX07423-004).
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Published In
Journal of Water Resources Planning and Management
Volume 139 • Issue 1 • January 2013
Pages: 23 - 33
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jan 31, 2011
Accepted: Dec 29, 2011
Published online: Jan 2, 2012
Published in print: Jan 1, 2013
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