Multilevel Factorial Fractional Programming for Sustainable Water Resources Management
Publication: Journal of Water Resources Planning and Management
Volume 142, Issue 12
Abstract
The need for more efficient water use has increased in importance with growing water scarcity and increasing competition among water users. Measuring the economic efficiency of water use has become a useful indicator for water resources management at all levels. This study proposes a multilevel factorial fractional programming model to support water resources management under uncertainty. Linear fractional programming is introduced to provide a practical way for taking into account the ratio of economic benefit to water consumption in the modeling process. This approach allows water allocation plans to be developed on the basis of the optimal economic efficiency of water use rather than economic incentives. A multilevel factorial analysis technique is integrated within linear fractional programming framework to deal with data uncertainty. This technique can quantify the individual and interactive effects of uncertain parameters on system performance and help decision makers gain improved insight into a changing system. To demonstrate its applicability, the model is applied to Xingshan County in China. A set of decision alternatives with respect to the optimal economic efficiency of water use are provided. The effects of uncertain parameters are quantified through a detailed uncertainty analysis, and the most influential parameters are identified.
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Acknowledgments
This research was supported by the National Sciences Foundation (51190095, 51225904), the 111 Project (B14008), and the Natural Science and Engineering Research Council of Canada. The authors thank the editors and anonymous reviewers for their helpful and insightful suggestions on the manuscript.
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Published In
Journal of Water Resources Planning and Management
Volume 142 • Issue 12 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Nov 16, 2015
Accepted: Jun 21, 2016
Published online: Aug 23, 2016
Published in print: Dec 1, 2016
Discussion open until: Jan 23, 2017
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