Volume 27, Issue 2 (Summer 2023)                   jwss 2023, 27(2): 71-90 | Back to browse issues page

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Taheri E, Mousavi F, Karami H. Optimization of Dam Reservoir Operation Using Metaheuristic Algorithms under Meteorological Drought Conditions (Case Study: Aydoghmoush Dam). jwss 2023; 27 (2) :71-90
URL: http://jstnar.iut.ac.ir/article-1-4295-en.html
Semnan University , fmousavi@semnan.ac.ir
Abstract:   (354 Views)
One of the basic steps in water resources management and planning according to population increase and lack of water resources in Iran is to optimize the use of dam reservoirs. In this research, the effect of meteorological droughts on the optimization of the Aydoghmoush dam reservoir in the northwest of Iran was evaluated by applying metaheuristic algorithms under the impact of future climate change. Three models and two scenarios of SSP2-4.5 and SSP2-8.5 of the sixth IPCC report, and the LARS-WG downscaling model were used for Aydoghmoush dam weather station for the base period (1978-2014) and future periods of 2022-2040 and 2070-2100. The inflow and outflow of the dam, as well as the optimal utilization of the dam reservoir, were evaluated using standalone, and hybrid mode of genetic, slime mold, and ant colony algorithms. Results of the best release scenario (SSP2-8.5) showed that the annual rainfall in the future periods will decrease by 8.9 mm, and 14.5 mm, respectively, compared to the base period. The objective function of optimizing the use of the dam reservoir was defined as minimizing the sum of squared relative deficiencies in each month and maximizing the reliability in the statistical period of 2011-2021. The results showed that in terms of time reliability, vulnerability, and stability, the hybrid slime mold-genetic algorithm was better than other algorithms with values of 0.73, 0.32, and 28.78. Prediction of the dam's inflow and outflow using the hybrid slime mold-genetic algorithm indicated high accuracy compared to other models by 13% and 19% errors, respectively.
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Type of Study: Research | Subject: Ggeneral
Received: 2022/09/8 | Accepted: 2023/01/9 | Published: 2023/09/1

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