Fuzzy Inference System for Robust Rule-Based Reservoir Operation under Nonstationary Inflows
Publication: Journal of Water Resources Planning and Management
Volume 143, Issue 4
Abstract
Although a number of adaptation strategies for coping with the ever-growing problem of inflow nonstationarity have been proposed in the reservoir operation literature, the robustness of reservoir operating rules to inflow nonstationarity has largely been ignored. This present study fills this gap. Fuzzy inference system based reservoir operating rules (FIS-ORs) are developed by optimization-simulation and compared with linear, nonlinear, and artificial neural network rules under simulated stationary and nonstationary inflow conditions. Results are obtained for two case studies assuming, in each case, a single reservoir with water supply, flood control, and environmental flow allocation functions. The FIS-ORs are found to be most robust to inflow nonstationarity. Applying the FIS-ORs to 30 years of projected future inflows show their advantage to be enhanced when they are recalibrated every 10 years or so, as compared to when there is no recalibration. It is also observed the advantage of the FIS-ORs to be more significant when the weighting of the problem objectives is such that the overall objective function is more difficult to satisfy.
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Acknowledgments
The authors thank Rashi Bhushan for inflow and water demand data. The authors also thank three anonymous reviewers for their comments, which have been crucial in improving this paper. This work is supported by project no. 617012 of the General Research Fund (GRF) of the Research Grants Council (RGC) of Hong Kong.
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Published In
Journal of Water Resources Planning and Management
Volume 143 • Issue 4 • April 2017
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©2016 American Society of Civil Engineers.
History
Received: Jan 15, 2016
Accepted: Sep 21, 2016
Published online: Nov 30, 2016
Published in print: Apr 1, 2017
Discussion open until: Apr 30, 2017
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