بررسی عمل‌کرد الگوریتم گرگ خاکستری و بهینه‌سازی ازدحام ذرات در مدیریت مخزن ناشی از تغییر اقلیم با اهداف مختلف بهره‌برداری | ||
| منابع آب و تغییر اقلیم | ||
| دوره 1، شماره 2، تیر 1404، صفحه 63-76 اصل مقاله (1.59 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22091/wrcc.2025.12863.1013 | ||
| نویسندگان | ||
| پریساسادات آشفته* 1؛ پریسا پورپشنگ2؛ نگین نبات قدس3 | ||
| 1گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران. | ||
| 2کارشناس ارشد، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران. | ||
| 3دانشجوی کارشناسی ارشد، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران. | ||
| چکیده | ||
| در این پژوهش تغییرات جریان ورودی به مخزن زاینده‌رود و تغییرات نیاز آبی شبکه پایاب آن با در نظر گرفتن بازه پایه (2010-1981) و تغییر اقلیم در دو بازه (2069-2040) و (2099-2070) تحت سناریوهای اقلیمی A2 و B2 با استفاده از HadCM3 بررسی می‌شود. تأثیرات این تغییر بر مصارف کشاورزی با استفاده از CROPWAT به‌دست می‌آید. هم‌چنین رواناب ورودی به مخزن با استفاده از شبکه عصبی مصنوعی (ANN) و IHACRES شبیه‌سازی می‌شود. نتایج حاکی از برتری ANN نسبت به IHACRES و کاهش رواناب در بازه‌های آتی نسبت به پایه و نیز افزایش تقاضاها دارد. به‌منظور بهینه‌سازی مخزن از دو الگوریتم گرگ خاکستری (GWA) و بهینه‌سازی ازدحام ذرات (PSO) برای کمینه کردن آسیب‌پذیری و بیشینه کردن اطمینان‌پذیری استفاده می‌شود. نتایج نشان دادند که GWO در بهینه کردن هریک از توابع هدف نسبت به PSO موفق‌تر بوده است. در A2 برای بازه (2069-2040) اطمینان‌پذیری برای GWA و PSO به‌ترتیب برابر با 89 و 88 درصد و آسیب‌پذیری به‌ترتیب برابر با 9.8 و 10.1 درصد و در (2099-2070) اطمینان‌پذیری به‌ترتیب برابر با 80 و 79 درصد و آسیب‌پذیری به‌ترتیب برابر 12.8 و 13.3 درصد حاصل شد. در B2 برای بازه (2069-2040) مقدار اطمینان‌پذیری برای دو مدل GWA و PSO به‌ترتیب برابر با 89 و 88 درصد و آسیب‌پذیری به‌ترتیب برابر با 10.4 و 10.7 درصد و در (2099-2070) اطمینان‌پذیری به‌ترتیب برابر با 76 و 73 درصد و مقدار آسیب‌پذیری به‌ترتیب برابر با 18.9 و 19.4 درصد حاصل شد. | ||
| کلیدواژه‌ها | ||
| الگوریتم ازدحام ذرات؛ الگوریتم گرگ خاکستری؛ بهینه‌سازی مخزن؛ تغییر اقلیم؛ منابع آب | ||
| عنوان مقاله [English] | ||
| Performance Evaluation of Grey Wolf Algorithm and Particle Swarm Optimization in Reservoir Management under Climate Change with Various Operational Objectives | ||
| نویسندگان [English] | ||
| Parisa-Sadat Ashofteh1؛ Parisa Pourpashang2؛ Negin Nabatghods3 | ||
| 1Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran. | ||
| 2Master’s Degree, Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran. | ||
| 3Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran. | ||
| چکیده [English] | ||
| Abstract Objective: In this study, changes in the inflow to the Zayandeh-Rud reservoir and changes in the water demand of its tributary network are investigated by considering the base period (1981-2010) and climate change in two periods (2069-2040) and (2099-2070) under climate scenarios A2 and B2 using HadCM3. Methods: The effects of this change on agricultural consumption are obtained using CROPWAT. Also, the inflow into the reservoir is simulated using artificial neural network (ANN) and IHACRES. In order to optimize the reservoir, two algorithms, Gray Wolf (GWA) and Particle Swarm Optimization (PSO), are used to minimize vulnerability and maximize reliability. Results: The results indicate the superiority of ANN over IHACRES and the reduction of runoff in future periods compared to the base period and the increase in demands. The results showed that in A2, for the period (2069-2040), the reliability for GWA and PSO was 89 and 88 percent, respectively, and the vulnerability was 9.8 and 10.1 percent, respectively, and in (2099-2070), the reliability was 80 and 79 percent, respectively, and the vulnerability was 12.8 and 13.3 percent, respectively. In B2, for the period (2069-2040), the reliability for the two GWA and PSO models was 89 and 88 percent, respectively, and the vulnerability was 10.4 and 10.7 percent, respectively, and in (2099-2070), the reliability was 76 and 73 percent, respectively, and the vulnerability was 18.9 and 19.4 percent, respectively. Conclusion: The results showed that GWO was more successful in optimizing each of the objective functions than PSO. English Extended Abstract Introduction This study focuses on optimizing the Zayandehrud Dam reservoir using two evolutionary algorithms—Grey Wolf Algorithm (GWA) and Particle Swarm Optimization (PSO)—to maximize reliability and minimize vulnerability in water resource management. Climate data, including temperature and precipitation, are extracted for the baseline period (1981–2010) and future intervals (2040–2069 and 2070–2099) using outputs from the HadCM3 model under two emission scenarios, A2 and B2. The impacts of climate change on water resources are assessed using the Artificial Neural Network (ANN) and IHACRES models. Additionally, the CROPWAT model analyzes climate change effects on water consumption and estimates irrigation demands. Method This study presents a comprehensive approach to optimizing the Zayandehrud Dam reservoir using single-objective evolutionary algorithms—particle Swarm Optimization (PSO) and the Grey Wolf Algorithm (GWA). The research focuses on maximizing reliability and minimizing vulnerability in reservoir operations while accounting for climate change effects. The study area, the Zayandehrud River Basin, is the first defined basin in the Central Plateau of Iran. Climate scenarios for temperature and precipitation are extracted from the HadCM3 model for the baseline period (1981–2010) and future intervals (2040–2069 and 2070–2099) under two emission scenarios, A2 and B2. Using the spatial coordinates of the meteorological station under investigation, the time series of temperature and precipitation variables corresponding to the HadCM3 computational grid cell are retrieved. To downscale climate variables, temperature and precipitation changes in future periods are calculated relative to the baseline and applied to observed data. Future climate variables are estimated by adding temperature scenarios to observed temperature values and multiplying precipitation scenarios with observed precipitation records. Future runoff simulations are conducted using the IHACRES and ANN models. The estimation of water demand and consumption volumes is performed using the CROPWAT model. Subsequently, reservoir optimization is carried out utilizing PSO and GWA, with reliability maximization and vulnerability minimization considered as objective functions. Results This study investigates the effects of climate change on rainfall, temperature, runoff, and water demand under A2 and B2 scenarios. Results indicate a notable temperature increase and reduced rainfall in the 2070-2099 compared to 2040-2069. Temperature and precipitation variations in the B2 scenario show a more significant temperature rise and precipitation reduction in the 2070-2099 than in 2040-2069. Runoff simulation results demonstrate that the Artificial Neural Network (ANN) outperforms IHACRES, leading to its adoption for future flow predictions. The simulated monthly long-term average runoff using the ANN model in the A2 scenario decreased by 15.7% and 20.9% in the 2040-2069 and 2070-2099 periods, respectively. In the B2 scenario, the reductions were 17.2% and 26.5% compared to the baseline period. Findings from the CROPWAT model indicate a rise in long-term average monthly water demand in future periods. Under scenario A2, demand increased by 100% and 170% in 2040-2069 and 2070-2099, respectively. Under scenario B2, demand increased by 94% and 121% compared to the baseline period. Water demand growth is more pronounced in the A2 scenario and in the 2070-2099 period compared to 2040-2069. The time series analysis of demand and release optimization using GWA and PSO shows the system's attempt to maximize supply and reduce vulnerability, thereby minimizing failure intensity. The results indicate that GWA performs better than PSO in achieving this objective. Conclusion This study examines the impact of climate change on water resources and consumption in the Zayandehrud Basin, focusing on future periods. Additionally, optimal reservoir operation strategies for the Zayandehrud Dam have been assessed. The findings indicate: Runoff under the A2 scenario will decrease by 15.7% in 2040-2069 and 20.9% in 2070-2099 while the B2 scenario will see reductions of 17.2% and 26.5%, respectively. Water demand will rise significantly, with an increase of 100% in 2040-2069 and 170% in 2070-2099 under A2, whereas B2 will exhibit a 94% increase in 2040-2069 and 121% in 2070-2099. Under baseline conditions, system reliability was 99.81% for GWA and 99.72% for PSO, while vulnerability was 0.53% for GWA and 0.71% for PSO. In the A2 scenario (2040-2069), reliability values for GWA and PSO were 89% and 88%, with corresponding vulnerability values of 9.8% and 10.1%. In A2 (2070-2099), reliability values for GWA and PSO declined to 80% and 79%, while vulnerability rose to 12.8% and 13.3%, respectively. In B2 (2040-2069), reliability values for GWA and PSO were 89% and 88%, while vulnerability was 10.4% and 10.7%, respectively. In B2 (2070-2099), reliability values dropped to 76% for GWA and 73% for PSO, with vulnerability increasing to 18.9% and 19.4%, respectively. Comparing both models reveals that GWA outperforms PSO by achieving higher reliability and lower vulnerability in reservoir operation. | ||
| کلیدواژه‌ها [English] | ||
| Particle Swarm Optimization, Grey Wolf Algorithm, Reservoir Optimization, Climate Change, Water Resources | ||
| مراجع | ||
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[1] Rong Q, Zhu Sh, Yue W, Su M, Cai Y. Predictive simulation and optimal allocation of surface water resources in reservoir basins under climate change. International Soil and Water Conservation Research. 2024 Jun; 12(2): 467-480. https://doi.org/10.1016/j.iswcr.2023.08.003. [2] Golfam P, Ashofteh PS, Loáiciga HA. Evaluation of the VIKOR and FOWA multi-criteria decision making methods for climate-change adaptation of agricultural water supply. Water Resources Management. 2019 May; 33: 2867-2884. https://doi.org/10.1007/s11269-019-02274-z. [3] Ahmadi R, Piri J, Galavi H, Keikha M. Comparing the efficiency of particle swarm and harmony search algorithms in optimizing the Muskingum–Cunge Model. Water. 2025 Jan; 17(1):104. https://doi.org/10.3390/w17010104. [4] Khoramipoor Z, Farzin S. A methodology to improving the performance of MOAHA optimization algorithm using chaos theory; Principle and application in optimal reservoir operation. Water Resources Management. 2025 Jan; 39: 2819-2840. https://doi.org/10.1007/s11269-025-04092-y. [5] Donyaii A, Sarraf A, Ahmadi H. Water reservoir multiobjective optimal operation using grey wolf optimizer. Shock and Vibration. 2020 Nov. https://doi.org/10.1155/2020/8870464. [6] Wang Q, Yue Ch, Li X, Liao P, Li X. Enhancing robustness of monthly streamflow forecasting model using embedded-feature selection algorithm based on improved gray wolf optimizer. Journal of Hydrology. 2023 Feb; 617: 128995. https://doi.org/10.1016/j.jhydrol.2022.128995. [7] Chong JY, Hooi GL, Goh QY, Lai V, Huang YF, Koo CH, El-Shafie A, Ahmed AN. Adapting reservoir operations for optimal water management under varying climate and demand scenarios using metaheuristic algorithms. Ain Shams Engineering Journal. 2024 Aug; 15(8): 102835. https://doi.org/10.1016/j.asej.2024.102835. [8] Liang J, Du Y, Xu Y, Xie B, Li W, Lu Z, Li R, Bal H. Using adaptive chaotic grey wolf optimization for the daily streamflow prediction. Expert Systems with Applications. 2024 Mar; 237: 121113. https://doi.org/10.1016/j.eswa.2023.121113. [9] Guo D, Zhang D, Xu D, Bian Y, Pan Y. Multi-objective water allocation for Wu’an City. Water. 2025 Jan; 17(2): 153. https://doi.org/10.3390/w17020153. [10] Ashofteh PS, Bozorg-Haddad O, Mariño MA. Climate change impact on reservoir performance indexes in agricultural water supply. Journal of Irrigation Drainage Engineering. 2013 Jul; 139(2): 85-97. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000496. [11] Jakeman AJ, Littlewood LG, Whitehead P. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology. 1990 Sep; 117(1-4): 275-300. https://doi.org/10.1016/0022-1694(90)90097-H. [12] McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics. 1943 Dec; 5(4): 115-133. https://doi.org/10.1007/978-3-642-70911-1_14. [13] Smith M. CROPWAT-A computer program for irrigation planning and management. Irrigation and Drainage Paper No. 46. Food and Agriculture Organization of the United Nations; 1992. [14] Eberhart RC, Kennedy J. Particle swarm optimization. Presented at the sixth international symposium on micro machine and human science; 1995 Oct; Perth, WA, Australia. https://doi.org/10.1109/MHS.1995.494215. [15] Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Advances in Engineering Software. 2014 Mar; 69: 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007. [16] Golfam P. Estimation of crop irrigation water demand using climatic data and Cropwat model. Water Resources and Climate Change. (2025) Mar; 1(1): 51-61. https://doi.org/10.22091/wrcc.2025.11311.1001. [17] Bagheri Khanghahi M, Hazar Jaribi A, Kamali MI, Zamani F. Forecasting rainfall in different climatic regions of Iran using the LARS WG7 climate model. Water Resources and Climate Change. (2025) Mar; 1(1): 28-39. https://doi.org/10.22091/wrcc.2025.11744.1008.
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