Application of Meta-Heuristic Algorithms in Reservoir Supply Optimization, Case Study: Mahabad Dam in Iran

Abstract

In arid and semi-arid areas, optimization and strategic planning of water delivery through an optimal and intelligently designed reservoir supply system is a primary task for water resources management. In this regard, the election algorithm (EA) is presented to estimate the optimal storage capacity of the Mahabad dam located in northwest Iran. EA is an intelligent iterative population-based algorithm that has recently been introduced for dealing with different optimization purposes. The capability of EA to address issues of local minimums in the feature search space is employed to yield a globally optimal explanation of the present issue. The data used in this study comprise 7-year (2008-2015) evaporation, rainfall, reservoir storage, reservoir inflows, and outflow. The results obtained from the EA approach are approximated with the continuous genetic algorithm (CGA). Based on the estimated results in the testing phase, an average relatively error (5.65%) is attained in the last implementation of the algorithm. The high efficacy of EA relative to the benchmark models in terms of the NSE and RMSE, MAE is found to be approximately 0.037, 0.41, and 0.74, respectively, which are less than the values of these criteria for the CGA. These error measures, i.e. NSE, MAE, and RMSE, for the CGA were calculated to be 0.66, 0.56, and 0.042, respectively. The obtained accurate results show the high performance of the EA model in estimating the optimal reservoir capacity and its efficiency in water resources management.

Keywords

• election algorithm
• continuous genetic algorithm
• optimization, reservoir storage
• mahabad dam