For Better Performance Please Use Chrome or Firefox Web Browser

Karamouz, M., Ahmadi, A., Moridi, A., "Probabilistic Reservoir Operation Using Bayesian Stochastic Model and Support Vector Machine", Advances in Water Resources, doi:10.1016/j.advwatres.2009.08.003, 32: 1588–1600, 2009.

Karamouz, M., Ahmadi, A., Moridi, A., "Probabilistic Reservoir Operation Using Bayesian Stochastic Model and Support Vector Machine", Advances in Water Resources, doi:10.1016/j.advwatres.2009.08.003, 32: 1588–1600, 2009.

 

 

Abstract

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.

The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.

Keywords

  • Reservoir operation;
  • Water quality management;
  • Genetic algorithm;
  • Support vector machine;
  • Nash bargaining theory;
  • Copula function

 

Link To Online Resource: 

http://www.sciencedirect.com/science/article/pii/S0309170809001304

 
Journal Papers
Month/Season: 
November
Year: 
2009