A hybrid Goal Programming method and Adaptive Neural-Fuzzy Inference System for Optimal Operation of a Multi-Objective Two-Reservoir System

Document Type : Original Article

Authors

1 Associate Professor, Faculty of Civil Engineering, Tabriz University, Tabriz, Iran

2 Master of Science Graduated, Faculty of Civil Engineering, Tabriz University, Tabriz, Iran

Abstract

Optimization of reservoirs operation is one of the most important tasks in the field of water resources management. In fact, vital requirement for beneficial use of water and energy resources clear the necessity of doing integrated planning and right operation of dams. recently, research has been made focusing on a shift from traditional single objective models to multi–objective models for the planning of multiple reservoir systems in a river basin. In this study the  three objectives of meeting irrigation and environmental demand, flood control and recreation (sometimes in conflict with each other) are  referred to for a two reservoir system by Goal Programming.
Within this framework, the mathematical model of two reservoirs system in Sefidrud watershed (Northern Iran) with the three objectives is formulated and the system parameters and decision variables are defined. The problem involves finding desired water releases from each reservoir in the system in order to satisfy the multiple objectives.
With comparing results of optimization models of this study, the model with the higher reliability indices was chosen as the best model. Due to the considerable advantages of linguistic rules in better inferring and interpreting the systems, an adaptive neural based fuzzy inference system (ANFIS) approach is used to consider uncertainties and to achieve a general method for multipurpose multi reservoir systems. The results of the Adaptive Neural Fuzzy Inference System (ANFIS) models shows that they can be applied successfully to provide high accuracy for the management of the reservoir systems.

Keywords

References

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History

  • Receive Date: 03 April 2010
  • Accept Date: 08 August 2012

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