![سامانه نشر مجلات علمی دانشگاه تهران](./data/logo.png)
تعداد نشریات | 162 |
تعداد شمارهها | 6,579 |
تعداد مقالات | 71,072 |
تعداد مشاهده مقاله | 125,681,386 |
تعداد دریافت فایل اصل مقاله | 98,911,614 |
کاربرد الگوریتم چندهدفه بهینه سازی ازدحام ذرات در بهره برداری کمی-کیفی از منابع آب مطالعه موردی: سد و رودخانه دز | ||
تحقیقات آب و خاک ایران | ||
دوره 55، شماره 3، خرداد 1403، صفحه 495-517 اصل مقاله (2.91 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijswr.2024.366954.669590 | ||
نویسندگان | ||
سعید فرخی1؛ محسن نجارچی* 2؛ حسین مظاهری3؛ سعید شعبانلو4 | ||
1دانشجوی دکتری عمران، گروه مهندسی عمران، واحد اراک، دانشگاه آزاد اسلامی ، اراک ، ایران | ||
2گروه مهندسی عمران، واحد اراک، دانشگاه آزاد اسلامی ، اراک ، ایران | ||
3گروه مهندسی شیمی، واحد اراک، دانشگاه آزاد اسلامی ، اراک ، ایران | ||
4دانشیار گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه،ایران | ||
چکیده | ||
در تحقیق حاضر سیستم منابع آب سطحی رودخانه دز حد فاصل سد تنظیمی دز تا بندقیر برای توسعه یک مدل کمی- کیفی که قادر به استخراج سیاستهای بهرهبرداری بهینه باشد انتخاب شد. برای شبیهسازی وضع موجود بهرهبرداری، تحت عنوان سناریوی مرجع، اتصال دینامیک بین مدلهای کمی و کیفی ایجاد شد. طوری که در سیستم کوپل شده، روابط هیدرولیکی بین تمام اجزای سیستم برقرار گردید. در سناریوی بهینهسازی متغیرهای تصمیم شامل نیاز زیست محیطی ماهیانه رودخانه و اهداف شامل حداکثرسازی درصد تامین نیازها و حداقل سازی تخطی از استانداردهای کیفی بودند. اجرای سناریوی بهینهسازی موجب افزایش اطمینانپذیری تأمین تمامی نیازهای دشت با هر اولویتی که دارند، گردید. همچنین نتایج سناریوی بهینهسازی نسبت به سناریوی مرجع نشان داد که نه تنها غلظت پارامترهای آلودگی و کیفی بهبود یافته است، بلکه در بسیاری از نقاط رودخانه بخصوص در محلهای برداشت آب کشاورزی، حداقل تجاوز از استانداردهای کیفی و آلودگی آب رودخانه وجود دارد. نتایج نشان داد با بهرهگیری از روش اتصال دینامیک کمی-کیفی منابع آب و توسعه مدل کوپلشده با استفاده از الگوریتم MOPSO، میتوان برنامهریزی بهتری برای استفاده مناسب از منابع آب موجود با در نظر گرفتن تمامی ذینفعان انجام داد. طوری که علاوه بر تأمین نیازها، روند کیفیت و آلودگی رودخانه نیز در طول دوره بهرهبرداری نزدیک به حدود استاندارد باشد. | ||
کلیدواژهها | ||
بهینه سازی چندهدفه؛ MOPSO؛ مدل کمی- کیفی؛ رودخانه دز | ||
عنوان مقاله [English] | ||
Application of multi-objective particle swarm optimization algorithm in quantitative-qualitative exploitation of water resources (Case study: Dez Dam and River) | ||
نویسندگان [English] | ||
saeid farokhi1؛ Mohsen Najarchi2؛ hossein mazaheri3؛ saeid shabanlou4 | ||
1Ph.D. Candidate, Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran, Iran | ||
2Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran | ||
3Department of Chemical Engineering, Arak Branch, Islamic Azad University, Arak, Iran | ||
4Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran. | ||
چکیده [English] | ||
The Dez-River's surface water resources system between the Dez regulatory dam and Bandar-e-Ghir is the focus of the current study to create a qualitative-quantitative-model that can be used to determine the best operating strategies. for replicate the existing operational state, a dynamic link between quantitative and qualitative models is built under the "reference-scenario" such that hydraulic linkages are generated between all of the system's components in the coupled system. The monthly environmental demands of the river are one of the choice factors in the optimization-scenario. The goals are to maximize the percentage of needs met and minimize quality standard violations. The implementation of the optimization scenario increased the reliability of providing all the needs of the plain with any priority. This problem illustrates how the reservoir should operate in an ideal state. In many places along the river, particularly the agricultural water withdrawal sites, the minimum violation of water quality standards has happened, according to a comparison of the pollution and quality parameters in the optimization scenario and the reference scenario. The amount of pollution and quality parameters has also improved. The findings show that it is possible to plan more effectively for the appropriate use of currently available water resources by taking into account all stakeholders and utilizing the qualitative-quantitative dynamic connection method of water resources to develop a coupled model using the MOPSO-algorithm. This will ensure that, in addition to meeting needs, the quality and pollution of the river remain close to the standard limits during the operation period. | ||
کلیدواژهها [English] | ||
Multi-objective optimization, MOPSO, Quantitative-qualitative model, Dez River | ||
مراجع | ||
Azari, A., Hamzeh, S., & Naderi, S. (2018). Multi-objective optimization of the reservoir system operation by using the hedging policy. Water Resour. Manage. 32 (6): 2061–2078. https://doi.org/10.1007/s11269-018-1917-5 Bayesteh, M and Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. J. Water Resour. Plann. Manage., 147(2), 04020099 Chapra, S., Pelletier, G., & Tao, H. (2006). QUAL2K: A Modeling Framework for Simulating River and Stream Water Quality (Version 2.04) Documentation. Civil and Environmental Engineering Department, Tufts University, Medford, MA. Chen, D., Chen, Q., Leon, A.S., & Li, R. (2016). A Genetic Algorithm Parallel Strategy for Optimizing the Operation of Reservoir with Multiple Eco-Environmental Objectives. Water Resources Management 30(7), 2127–2142. Coello, C. A., Pulido G. T., & Lechuga M. S. (2004). Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation Journal, 8(3), 256 – 279. Cox, B. 2003. A review of currently available in-stream water-quality models and their applicability for simulating dissolved oxygen in lowland rivers. Science of the total environment, 314, 335-377. Da Silva, T. D., & Albuquerque Alves, C. D. M. (2016). WEAP and QUAL2K Model Integration for Water Quality Evaluations as a Result of Urban Expansion Scenarios in the Federal District of Brazil. Paper presented at the World Environmental and Water Resources Congress 2016. Deksissa, T., Meirlaen, J., Ashton, P. J., & Vanrolleghem, P. A. 2004. Simplifying dynamic river water quality modelling: A case study of inorganic nitrogen dynamics in the Crocodile River (South Africa). Water, Air, and Soil Pollution, 155(1-4), 303-320. Fallahi, M.M., Shabanlou, S., Rajabi, A. Yosefvand, F., & IzadBakhsh, M.A. (2023). Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer). Appl Water Sci 13, 143. Goorani, Z., & Shabanlou, S. (2021). Multi-objective optimization of quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland. Journal of Environmental Management. 292, 112769. https://doi.org/10.1016/j.jenvman.2021.112769 Hassan, R., Cohanim, B., Weck, O.D. & Venter, G. (2005). A Comparison of Particle Swarm Optimization and the Genetic Algorithm. 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. 18-21 April 2005, Austin, Texas. Hu, M., Huang, G.H., Sun, W., Ding, X., Li, Y.P., & Fan, B. (2016). Optimization and Evaluation of Environmental Operations for Three Gorges Reservoir. Water Resources Management, 30(10), 3553–76. Jalili, A.A., Najarchi, M., Shabanlou, S., & Jafarinia R. (2023). Multi-objective Optimization of water resources in real time based on integration of NSGA-II and support vector machines. Environ Sci Pollut Res 30, 16464–16475. Jalilian, A., Heydari, M., Azari, A., & Shabanlou, S. (2022). Extracting Optimal Rule Curve of Dam Reservoir Base on Stochastic Inflow. Water Resources Management, 36, 1763–1782. https://doi.org/10.1007/s11269-022-03087-3 Jia, F., & Lichti, D. (2017). A comparison of simulated annealing, genetic algorithm and particle swarm optimization in optimal first-order design of indoor TLS networks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V(IV-2/W4), 18–22 September, Wuhan, China. Kachitvichyanukul, K. (2012). Comparison of Three Evolutionary Algorithms: GA, PSO, and DE. Industrial Engineering and Management Systems, 11(3), 215-223. Kannel, P. R., Lee, S., Lee, Y.-S., Kanel, S., & Pelletier, G. (2007). Application of automated QUAL2Kw for water quality modeling and management in the Bagmati River, Nepal. ecological modelling, 202(3), 503-517 Karamian, F., Mirakzadeh, A. A., & Azari, A. (2023). Application of multi-objective genetic algorithm for optimal combination of resources to achieve sustainable agriculture based on the water-energy-food nexus framework. Science of The Total Environment, 860, 160419. https://doi.org/10.1016/j.scitotenv.2022.160419 Mao, J., Zhang, P., Dai, L., Dai, H., & Hu, T., (2016). Optimal Opeartion of a Multi-Reservoir System for Environmental Water Demand of a River-Connected Lake. Hydrology Research 47, 206-224. Mishra, B. K., Regmi, R. K., Masago, Y., Fukushi, K., Kumar, P., & Saraswat, C. (2017). Assessment of Bagmati River Pollution In Kathmandu Valley: Scenario-Based Modeling and Analysis for Sustainable Urban Development. Sustainability of Water Quality and Ecology Moghadam, R.G., Shabanlou, S., & Yosefvand, F. (2020). Optimization of ANFIS Network Using Particle Swarm Optimization Modeling of Scour around Submerged Pipes. J. Marine. Sci. Appl. 19, 444–452. https://doi.org/10.1007/s11804-020-00166-y Nourbakhsh, A., Safikhani, H., & Derakhshan, S. (2011). The comparison of multi-objective particle swarm optimization and NSGA-II algorithm: applications in centrifugal pumps. Engineering Optimization, 43 (10), 1095–1113. Sulis, A., & Sechi, G. M. (2013). Comparison of generic simulation models for water resource systems. Environmental modelling & software, 40, 214-225. Yarmohammadi, E., Izadbakhsh, M.A., Rajabi, A., Yosefvand, F. & Shabanlou, S. (2022). Optimal operation of water resources systems using MOICA algorithm with reservoir hedging approach in low-water regions. Irrigation and Drainage, 71(2), 406–417. https://doi.org/10.1002/ird.2660 Yates, D., Sieber, J., Purkey, D., & Huber-Lee, A. (2005). WEAP21—A demand-, priority-, and preference-driven water planning model: part 1: model characteristics. Water International, 30(4), 487-500. Zarei, N., Azari, A., & Heidari, M. M. (2022). Improvement of the performance of NSGA-II and MOPSO algorithms in multi-objective optimization of urban water distribution networks based on modification of decision space. Applied Water Science, 12 (133), 1-12. Zeinali, M., Azari, A., & Heidari, M. M. (2020). Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. American Society of Civil Engineers. J. Water Resour. Plann. Manage., 146(5), 04020020 | ||
آمار تعداد مشاهده مقاله: 188 تعداد دریافت فایل اصل مقاله: 147 |