Fuzzy Neural Network Modeling of Reservoir Operation
Publication: Journal of Water Resources Planning and Management
Volume 135, Issue 1
Abstract
The present study aims at the application of the hybrid model, which consists of artificial neural network and fuzzy logic in the reservoir operating policy during critical periods. The proposed hybrid model [fuzzy neural network (FNN)] combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The FNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The FNN model has been developed to study the behavior of optimal release operating policy on the proposed reservoir in Pagladiya River of the Assam State in India. Here, reservoir operation policies were formulated through dynamic programming. The optimal release was related to storage, inflow, and demand. The advantages of using the FNN model in reservoir release are discussed using the case study.
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Acknowledgments
The writers wish to acknowledge the support given by the Brahmaputra Board, Assam by providing the necessary data for the analysis.
References
Akter, T., and Simonovic, S. P. (2004). “Modeling uncertainties in short-term reservoir operation using fuzzy sets and a genetic algorithm.” Hydrol. Sci. J., 49(6), 1081–1097.
Badiru, A. B., and Cheung, J. Y. (2002). Fuzzy engineering expert systems with neural network applications, Wiley, New York.
Bessler, F. T., Savic, D. A., and Walters, G. A. (2003). “Water reservoir control with data mining.” J. Water Resour. Plann. Manage., 129(1), 26–34.
Brown, M., and Harris, C. (1994). Neuro-fuzzy adaptive modeling and control, Prentice-Hall, Englewood Cliffs, N.J.
Cancelliere, A., Giuliano, G., Ancarani, A., and Rossi, G. (2002). “A neural networks approach for deriving irrigation reservoir operating rules.” Water Resour. Manage., 16, 71–88.
Chandramouli, V., Kuppusamy, K. A., and Manikandan, K. (2002). “Study on water sharing in a multi-reservoir system using a dynamic programming-neural network model.” Int. J. Water Resour. Dev., 18(3), 425–438.
Chandramouli, V., and Raman, H. (2001). “Multireservoir modeling with dynamic programming and neural networks.” J. Water Resour. Plann. Manage., 127(2), 89–98.
Chang, F. J., and Chang, Y. T. (2006). “Adaptive neuro-fuzzy inference system for prediction of water level in reservoir.” Adv. Water Resour., 29(1), 1–10.
Chang, L. C., and Chang, F. J. (2001). “Intelligent control for modeling of real time reservoir operation.” Hydrolog. Process., 15(9), 1621–1634.
Chang, Y. T., Chang, L. C., and Chang, F. J. (2005). “Intelligent control for modeling of real time reservoir operation. Part II: ANN with operating curves.” Hydrolog. Process., 19, 1431–1444.
Chaves, P., and Kojiri, T. (2007). “Conceptual fuzzy neural network model for water quality simulation.” Hydrolog. Process., 21(5), 634–646.
Deka, P., and Chandramouli, V. (2003). “A fuzzy neural network model for deriving the river stage-discharge relationship.” Hydrol. Sci. J., 48(2), 197–209.
Deka, P., and Chandramouli, V. (2005). “Fuzzy neural network model for hydrologic flow routing.” J. Hydrol. Eng., 10(4), 302–314.
Dubrovin, T., Joima, A., and Turunen, E. (2002). “Fuzzy model for real-time reservoir operation.” J. Water Resour. Plann. Manage., 128(1), 66–73.
Esogbue, A., and Kacprzyk, J. (1998). “Fuzzy dynamic programming.” Decision analysis, operation research and statistics, Handbook of Fuzzy Sets Series, Vol. 5, Kluwer, Boston, 281–307.
Fontane, D. G., Gates, T. K., and Moncada, E. (1997). “Planning reservoir operations with imprecise objectives.” J. Water Resour. Plann. Manage., 123(3), 154–163.
Jain, S. K., Das, A., and Srivastava, D. K. (1999). “Application of ANN for reservoir inflow prediction and operation.” J. Water Resour. Plann. Manage., 125(5), 263–271.
Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). “Neural networks for river flow prediction.” J. Comput. Civ. Eng., 8(2), 201–220.
Kosko, B. (1996). “Neural networks and fuzzy systems.” Prentice-Hall of India (original edition: Prentice-Hall Inc., Englewood Cliffs, N.J., 1992).
Labadie, J. W. (2004). “Optimal operation of multi-reservoir systems: State-of-the-art review.” J. Water Resour. Plann. Manage., 130(2), 93–111.
Mousavi, S. J., Ponnambalam, K., and Karray, F. (2005). “Reservoir operation using a dynamic programming fuzzy rule-based approach.” Water Resour. Manage., 19(5), 655–672.
Raman, H., and Chandramouli, V. (1996). “Deriving a general operating policy for reservoirs using neural network.” J. Water Resour. Plann. Manage., 122(5), 342–347.
Russell, S. O., and Campbell, P. F. (1996). “Reservoir operating rules with fuzzy programming.” J. Water Resour. Plann. Manage., 122(3), 165–170.
Shrestha, B. P., Duckstein, L., and Stakhiv, E. Z. (1996). “Fuzzy rule-based modeling of reservoir operation.” J. Water Resour. Plann. Manage., 122(4), 262–269.
Teegavarapu, R. S. V., and Simonovic, S. P. (1999). “Modeling uncertainty in reservoir loss functions using fuzzy sets.” Water Resour. Res., 35(9), 2815–2823.
Tilament, A., Fortemps, P., and Vanclooster, M. (2002). “Effect of averaging operators in fuzzy optimization of reservoir operation.” Water Resour. Manage., 16, 1–22.
Yakowitz, S. (1982). “Dynamic programming applications in water resources.” Water Resour. Res., 18(4), 673–696.
Yeh, W. W.-G. (1985). “Reservoir management and operations models: A state-of-the-art review.” Water Resour. Res., 21(12), 1797–1818.
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Published In
Journal of Water Resources Planning and Management
Volume 135 • Issue 1 • January 2009
Pages: 5 - 12
Copyright
© 2009 ASCE.
History
Received: Oct 13, 2006
Accepted: Jun 4, 2008
Published online: Jan 1, 2009
Published in print: Jan 2009
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