Abstract
In this article, a fuzzy rule based model is developed for the operation of a single purpose reservoir. The model operates on an 'if – then' principle, where the 'if' is a vector of fuzzy premises and the 'then' is a vector of fuzzy consequences. The steps involved in the development of the model include, construction of membership functions for the inflow, storage, demand and the release, formulation of fuzzyrules, implication and defuzzification. The methodology is illustrated through the case study of the Malaprabha irrigation reservoir in Karnataka, India. Reservoir storage, inflow, and demands are used as premises and the release as the consequence.Simulated reservoir operation with a steady state policy provides the knowledge base necessary for the formulation of the Fuzzy rules.
Similar content being viewed by others
References
Bardossy, A. and Disse, M.: 1993, Fuzzy rule based models for infiltration,Water Resour. Res. 29(2), 373–382.
Civanlar, M. R. and Trussel, H. J.: 1986, Constructing membership functions using statistical data, Fuzzy Sets and Systems 18, 1–13.
Devi, B. B. and Sharma, V. V. S.: 1985, Estimation of fuzzy membership from histograms, Information Sci. 35(1), 43–59.
Dombi, J.: 1990, Membership function as an evaluation, Fuzzy Sets and Systems 35, 1–21.
Fontane, D. G., Gates, T. G. and Moncada, E.: 1997, Planning reservoir operations with imprecise objectives, J. Water Resour. Plann. Manag. 123(3), 154–162.
Hashimoto, T., Louks, D. P. and Stedinger, J. R.: 1982, Reliability, resiliency, and vulnerability criteria for water resources system performances and evaluation, Water Resour. Res. 18(1), 14–20.
Hellendoorn, H. and Thomas, C.: (1993), Defuzzification in fuzzy controllers, J. Intelligent Fuzzy Syst. 1(2), 109–123.
Kindler, J.: (1992), Rationalizing water requirements with aid of fuzzy allocation model, J. Water Resour. Plann. Manag. 118(3), 308–323.
Klir, G. J. and Folger, T. A.: 1995, Fuzzy sets, Uncertainty, and Information, 4th edn, Prentice Hall of India Limited (original edition: Prentice Hall Inc., Englewood Cliffs, 1988).
Klir, G. J. and Yuan, Bo: 1997, Fuzzy Sets and Fuzzy Logic, Prentice Hall of India (original edition: Prentice Hall Inc., Englewood Cliffs, 1995).
Kosko, B.: 1996, Neural Networks and Fuzzy Systems, Prentice Hall of India (original edition: Prentice Hall Inc., Englewood Cliffs, 1992).
MATLAB: 1995, Fuzzy Logic Toolbox, The Math Works Inc.
Mujumdar, P. P. and Vedula, S.: (1992), Performance evaluation of an irrigation system under some optimal operating policies, Hydrol. Sci. J. 37(1), 13–26.
Mujumdar, P. P. and Ramesh, T. S. V.: 1997, Real-time reservoir operation for irrigation, Water Resour. Res. 33(5), 1157–1164.
Ross, T. J.: 1997, Fuzzy Logic with Engineering Applications, McGraw Hill International Editions, Electrical Engineering Series.
Russel, S. O. and Campbell, P. E.: 1996, Reservoir operating rules with fuzzy logic programming, J. Water Resour. Plann. Manag. 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. Manag. 122(4), 262–269.
Simonovic, S. P.: 1992, Reservoir system analysis: Closing the gap between theory and practice, J. Water Resour. Plann. Manag. 118(3), 262–280.
Vedula, S. and Mujumdar, P. P.: (1992), Optimal reservoir operation for irrigation of multiple crops, Water Resour. Res. 28(1), 1–9.
Yager, R. R. and Filev, D.: (1993), On the issue of defuzzification and selection based on a fuzzy set, Fuzzy Sets and Systems 55, 255–271.
Yeh, W.,W.-G.: 1985, Reservoir management and operation models: A state of the art review, Water Resour. Res. 21(12), 1797–1818.
Zadeh, L. A.: 1965, Fuzzy sets, Information and Control 8, 338–353.
Zimmermann, H. J.: (1996), Fuzzy Set Theory and Its Application, Allied Publishers Limited, New Delhi (original edition: Kluwer Academic Publishers, 1991).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Panigrahi, D.P., Mujumdar, P.P. Reservoir Operation Modelling with Fuzzy Logic. Water Resources Management 14, 89–109 (2000). https://doi.org/10.1023/A:1008170632582
Issue Date:
DOI: https://doi.org/10.1023/A:1008170632582