ترکیب بهینه حجم و ظرفیت خروجی حوضچه تاخیری با استفاده از بهینه‌سازی ازدحام ذرات و مدل تحلیلی- احتمالاتی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی‌ارشد مهندسی منابع آب، دانشگاه شهید باهنر، کرمان، ایران

2 دانشیار گروه مهندسی آب، دانشگاه تبریز، تبریز، ایران

3 دانشجوی کارشناسی‌ارشد سازه‌های آبی، دانشگاه تبریز، تبریز، ایران

چکیده

حوضچه‌های تأخیری بهترین راه‌کارهای مدیریتی طراحی شده برای کنترل سیلاب شهری هستند و اهداف طراحی آن‌ها عمدتا برای کنترل کمیت و کیفیت سیلاب به‌ازای هزینه کمینه می‌باشد. در این پژوهش، داده‌های طولانی‌مدت بارش ساعتی ایستگاه‌های سینوپتیک کرمان و مهرآباد مورد تجزیه و تحلیل قرار گرفت و سه مشخصه بارش (مدت زمان بارش، حجم بارش و زمان بین دو رخداد بارش) تعیین شد. مدل‌های تحلیلی- احتمالاتی (APM) به‌کار گرفته شد و پارامترهای مدل با استفاده از نزدیک‌ترین ایستگاه باران‌سنجی به منطقه مورد مطالعه تخمین زده شدند. پارامترهای مدل تحلیلی- احتمالاتی، به‌همراه پارامتر حوضه آبخیز، به‌منظور ایجاد ترکیبی بهینه از حجم حوضچه و ظرفیت خروجی مورد استفاده قرار گرفتند. تابع هدف بصورت کمینه سازی هزینه­ها با استفاده از مدل تحلیلی- احتمالاتی و بهینه‌سازی ازدحام ذرات (PSO) تعیین گردید. مقایسه بهینه‌سازی ازدحام ذرات با مدل تحلیلی- احتمالاتی نشان می‌دهد که نتایج PSO برای هزینه متغیرهای طراحی شده از دقت بالاتری برخوردار است و نیازی به اندازه ظرفیت خروجی حوضچه ندارد. همچنین نتایج نشان داد که مدل PSO، مدلی مقرون به صرفه و سریع برای اجرا خواهد بود.

کلیدواژه‌ها


عنوان مقاله [English]

Optimum Combination of Volume and Outlet Capacity of a Detention Pond using Particle Swarm Optimization and Analytical-Probabilistic Model

نویسندگان [English]

  • M Moradi 1
  • A Abbaspour 2
  • S Azizi 3
  • S Darbandi 2
1 M. Sc. Student, Graduate of Water Resources Eng., Dept. of Water Engineering, Shahid Bahonar Univ., Kerman, Iran
2 Associ.. Prof., Dept. of Water Eng., Faculty of Agric., University of Tabriz, Tabriz, Iran
3 M. Sc. Student, Hydraulic Structures, Dept. of Water Eng., Faculty of Agriculture, Univ. of Tabriz, Tabriz, Iran
چکیده [English]

Detention ponds are best management practices designed for the control of urban stormwater and their design objectives are mainly controlling the quantity and quality of urban stormwater at the minimum cost. In this research, long term hourly rainfall data of Kerman and Mehrabad synoptic stations were analyzed and the three rainfall characteristics (rainfall duration, rainfall volume and inter event time duration) were obtained. Analytical probabilistic models (APM) were employed and the model parameters were derived from the closest stations to the study areas. The APM parameters, along with the catchment parameters, were used to develop the optimal combination of the pond volume and outlet capacity. Also, the PSO model led to lower computational   costs in comparison with the APM model. Comparison of the PSO with APM showed that the PSO results were more accurate than the results of APM and there was no need for determination capacity in the forementional model as it did not need outlet size. The PSO model was also found to be more computationally implemented more cheaper and faster.

کلیدواژه‌ها [English]

  • Analytical probabilistic models
  • Detention pond
  • particle swarm optimization
  • Runoff
  • Urban watershed
Adams BJ and Papa F, 2000. Urban stormwater management planning with analytical probabilistic Models. Wiley, New York, NY.
Ahmadisharaf E and Tajrishy M, 2015. Siting Detention Basins Using SWMM and Spatial Multi-Criteria Decision Making. Journal of Water and Wastewater 25(6): 57-66.(Persian)
Aziz NAA, Alias MY, Mohemmed AW and Aziz KA, 2011. Particle swarm optimization for constrained and multiobjective problems: A brief review. International conference on management and artificial intelligence IPEDR, Bali, Indonesia 6: 146–150.
Baltar AM and Fontane DG, 2008. Use of multiobjective particle swarm optimization in water resources management. Journal of Water Resources Planning and Management, ASCE 134: 257–265.
Butler D and Davies JW, 2004. Urban Drainage. 2nd edition. Spon Press, Tailor & Francis Group. New York.
Chen J and Adams BJ, 2006. Urban stormwater quality control analysis with detention ponds. Water Environment Research 78: 744–753.
Chen J and Adams BJ, 2007. Development of analytical models for estimation of urban stormwater runoff. Journal of Hydrology 336: 458- 469.
Cunha MC, Zeferino JA, Simões NE and Saldarriaga JG, 2016. Optimal location and sizing of storage units in a drainage system. Environmental Modelling & Software 30(83): 155-166.
Fallah-Mehdipour EF, Haddad OB and Marin˜o MA, 2009. MOPSO in multipurpose operation of single-reservoir system. In: ASCE, ed. World Environmental and Water Resources Congress, Kansas City, Missouri, USA: Great Rivers, ASCE.
Ghasemi S and Faghfour Maghrebi M, 2015. Delay ponds as a solution for sustainable urban development and management. Journal of Rainwater Catchment Systems 3(1): 1-14.(Persian)
Guo JCY and Urbonas B, 2002. Runoff capture and delivery curves for storm-water quality control designs. Journal of Water Resources Planning and Management, ASCE 12: 208–215.
Guo Y, 2001. Hydrologic design of urban flood control detention ponds. Journal of Hydrologic Engineering, ASCE 6: 472–479.
Jeng HAC, Englande AJ, Bakeer, RM and Bradford, H., 2005. Impact of urban stormwater runoff on estuarine environmental quality. Estuarine, Coastal and Shelf Science 63: 513–526.
Jeong S, Hasegawa S, Shimoyama K and Obayash S, 2009. Development and investigation of efficient GA/PSO-hybrid algorithm applicable to real-world design optimization. IEEE Computational Intelligence Magazine 36–44.
Karami  M, Ardeshir A and Behzadian K, 2016. Hazard  management of inundation and pollutants in urban floods using optimal conventional and novel strategies. Iran Water Resources Research 11(3): 100-112.(Persian)
Kennedy J and Eberhart RC, 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks IV. 27 November–1 December, NJ, USA: IEEE Service Center Piscataway, Perth, Australia.
Lee JG, Heaney JP and Lai FH, 2005. Optimization of integrated urban wet-weather control strategies. Journal of Water Resources Planning and Management, ASCE 13: 307–315.
Li JY and Adams BJ, 2000. Probabilistic models for analysis of urban runoff control systems. Journal of Environmental Engineering, ASCE 126: 217–224.
Mccuen RH, 2004. Hydrologic Analysis and Design. Upper Saddle River, NJ: Prentice Hall.
Mobley JT and Culver TB, 2012. Design of outlet control structures for ecological detention ponds. Journal of Water Resources Planning and Management 140(2): 250-257.
Moradi M,Darbandi S, Darbandi S, 2018. Development of analytical-probabilistic models for estimating urban storm
water runoff, Water and Soil Science 4(28):1-16.(Persian).
Muñoz Zavala AE, Aguirre AH and Villa Diharce ER, 2005. Constrained optimization via particle evolutionary swarm optimization algorithm (PESO).Pp.209-219. In Proceedings of the 7th Annual Conference on Genetic and evolutionary computation.
Nascimento NO, Ellis JB, Baptista MB and Deutsch JC, 1999. Using detention basins: operational experience and lessons. Urban water 1: 113–124.
Ngo TT, Yoo DG, Lee YS and Kim JH, 2016. Optimization of upstream detention reservoir facilities for downstream flood mitigation in urban areas. Journal of Water 14: 8(7):290.
Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M, Minsker B, Ostfeld Singh A and Zechman E, 2010. State of the art for genetic algorithms and beyond in water resources planning and management. Journal of Water Resources Planning and Management 136: 412–432.
Papa F, Adams BJ and Guo Y, 1999. Detention time selection for stormwater quality control ponds. Canadian Journal of Civil Engineering 26: 72–82.
Park D, Jang S and Roesner LA, 2007. Multipurpose detention pond design for improved watershed management. Pp.11-20. In Proceedings of the World Environmental and Water Resources Congress, Florida, 15-19 May.
Parsopoulos KE and Vrahatis MN, 2010. Particle swarm optimization and intelligence: advances and applications. New York: Information Science Reference.
Rao SS, 2009. Engineering Optimization: Theory and Practice. 4th edition. New Jersey: John Wiley & Sons Inc.
Salajegheh A, Forootan E, Mahdavi M, Ahmadi H, Sharifi F and Malek Mohammadi B, 2012. Runoff estimation in urban watersheds by analytical models (case study: The part of district No.22 of Tehran city), Journal of Water and Wastewater 23(1): 47-56.(Persian)
Saribabu CR and Neelakantan TR, 2006. Design of water distribution networks using particle swarm optimization. Urban Water Journal 3 (2): 111–120.
Shahapure SS, Eldho TI and Rao EP, 2011. Flood Simulation in an Urban Catchment of Navi Mumbai City with Detention Pond and Tidal Effects using FEM, GIS, and Remote Sensing. Journal of Waterway, Port, Coastal, and Ocean Engineering 137(6): 286-299.
Shokoohi A and Daneshvar S, 2007. Flood Control in Urban Basins Using Detention Ponds Comparison to Localized River Engineering Countermeasures. Iran Water Resources Research 3(1): 80-83.(Persian)
Steg R, 2010. Summary of the stormwater sources in the Flathead Lake basin. Helena, MT, USA: USEPA, Montana Operations Office.(Persian)
Tung YK, 1988. Multi-objective detention basin design in urban drainage systems - tradeoff between risk and cost. Journal of Water Resources Management 2: 51–62.
Unmarked, 2016, Price Catalog for irrigation and drainage-water engineering, Management and Planning Organization of Islamic Republic of Iran,
Yaghini M, Akhavan Kazemzadeh MR, 2016, Meta-heuristic optimization algorithms, Amirkabir University of Technology, Tehran.(Persian)
Yu PS, Yang TC, Kuo CM and Tai CW, 2015. Integration of physiographic drainage-inundation model and nondominated sorting genetic algorithm for detention-pond optimization. Journal of Water Resources Planning and Management 141(11): 1-11.
Zhao J, Zhao Y, Zhao X and Jiang C, 2016. Agricultural runoff pollution control by a grassed swales coupled with wetland detention ponds system: a case study in Taihu Basin, China. Environmental Science and Pollution Research 1; 23(9): 90-104.