Optimal Utilization of Water Resources in Real Time Based on NSGA-II Algorithms and Support Vector Machines (Case Study: Gavoshan Dam)

Document Type : Research Paper


1 Assistant Professor, Department of Water Engineering, Razi University

2 Assistant Professor, Department of Water Engineering, Razi University.


Ample research has been done on optimization methods for the exploitation of reservoirs in the form of a specific optimization. In this type of studies, a certain series of flow is provided for the reservoir during the operation period and the release from the reservoir to downstream is optimized under such circumstances. They are certain drawbacks for these models. First, optimal solutions cannot be generalized for other possible inputs to the reservoir. Second, in the event of a change in the flow of input into the reservoirs, it is likely that the optimal solutions are not efficient and the operation of the system in the form of an optimization algorithm should be performed again. In such circumstances, one of the solutions is the use of intelligent methods such as support vector machines to apply the results of system optimization in real time. The main goal of this study is to integrate artificial intelligence methods such as support vector machine with NSGA-II optimization algorithm to convert it to real time. In this structure, contrary to the conventional design of the scheduling, in the event of a change in the flow of input, there is no need to perform re-optimization to understand the optimal coefficients. Instead, the extraction relationship of the support vector machine can be based on the input to the reservoir (at the beginning of the month), the volume of water storage in the tank (at the beginning of the month), reservoir storage changes and downstream needs in the current month to yield optimal release rates in real time.


Main Subjects

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Volume 43, Issue 1
March 2020
Pages 189-204
  • Receive Date: 12 September 2017
  • Revise Date: 05 June 2018
  • Accept Date: 09 June 2018
  • Publish Date: 20 March 2020