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Hybridizing ANN-NSGA-II model with genetic programming method for reservoir operation rule curve determination (Case study Zayandehroud dam reservoir)

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Abstract

One of the most important and effective works of water resource planning and management is determining the specific, applicable, regulated operating policies of the Zayandehroud dam reservoir, as a case study, in which it should be user-friendly and straightforward for the operator. For this purpose, different methods have been proposed in which each of them has its limitations. Due to the unique capabilities of the genetic programming (GP) model, here, this method is used to determine the operating rule curves and policies of the dam reservoir. For this purpose, here, two cases are proposed in which, in the first case, each month is individually simulated and modeled. However, in the second case, all months are simulated simultaneously. A second case is proposed here to determine simple and more applicable operation rule curves. In addition, two approaches are suggested for each case in which in the first approach, the influential input variables are selected by presenting the hybrid method. In the proposed hybrid method, the artificial neural network (ANN) model is equipped with non-dominated sorting genetic (NSGA-II) algorithm leading to a hybrid method named the ANN-NSGA-II method. However, in the second approach, the influential input variables are selected automatically using the GP method. Here, the hybrid method is proposed and used to overcome the limitations of existing usual method. In other words, it is proposed to reduce the number of influential input variables of data-driven methods and select the effective ones. The obtained results of all proposed cases and approaches are presented and compared with the standard operation policy method, stochastic dynamic programming, ANN model, and NLP method. Comparison of the results shows the acceptable performance of the proposed cases and approaches. In other words, the best-obtained values of (stability index) SI index and water deficit (objective function value) are 49.3% and 32, respectively.

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  1. Department of Civil Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, 81746-73441, Iran

    Ramtin Moeini & Kamran Nasiri

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  1. Ramtin Moeini
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  2. Kamran Nasiri
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Correspondence to Ramtin Moeini.

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Moeini, R., Nasiri, K. Hybridizing ANN-NSGA-II model with genetic programming method for reservoir operation rule curve determination (Case study Zayandehroud dam reservoir). Soft Comput 25, 14081–14108 (2021). https://doi.org/10.1007/s00500-021-06130-4

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