دمای آب مخزن سد کرخه با استفاده از الگوریتم بهینه‌ساز PSO

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی محیط زیست، دانشکده مهندسی عمران، دانشگاه علم و صنعت

2 استاد دانشکده عمران، گروه مهندسی آب، دانشگاه علم و صنعت

3 کاندیدای دکترای مدیریت کیفی منابع آب، دانشکده مهندسی عمران، دانشگاه علم و صنعت

چکیده

در این مطالعه از الگوریتم بهینه‌سازی ذرات تجمعی برای کالیبراسیون اتوماتیک پارامترهای مؤثر در شبیه‎سازی دما در مدل CE-QAUL-W2 استفاده شد. الگوریتم PSO به‌عنوان یک بهینه‌ساز تابع هدف فرایند اتوماتیک کالیبراسیون دما را بهینه می‌کند. تابع هدف در این مطالعه مجموع قدرمطلق خطا بین داده‌های شبیه‌سازی شده توسط مدل شبیه‌ساز و داده‌های اندازه‎گیری شده از مخزن سد کرخه در نقاط کنترل و در طی روزهای پایش بود. با تحلیل حساسیت پارامترهای مؤثر در کالیبراسیون دما در این مطالعه، ضریب پوشش ابر، ضریب محو شدن نور در آب، ضریب جذب نور در سطح آب و ضرایب تجربی تابع سرعت باد AFW، BFW و CFW به‌عنوان پارامترهای مؤثر در کالیبراسیون دما مطرح هستند. در ادامه با استفاده از داده‎های مشاهداتی فرضی، کارایی مدل کالیبراسیون اتوماتیک PSO-CE-QUAL-W2 مورد بررسی قرار گرفت و درستی عملکرد آن تأیید شد. سپس مدل اتوماتیک کالیبراسیون دما به‌منظور کالیبراسیون دما در طی 90 روز در مخزن کرخه با داده‎های میدانی به‌کار گرفته شد. نتایج نشان دهنده همگرایی بسیار مناسب داده‎های مشاهداتی و میدانی در طول دوره شبیه‎سازی بود.

کلیدواژه‌ها


عنوان مقاله [English]

Automatic Thermal Calibration of Two Dimensional CE-QUAL-W2 Model in Karkheh Reservoir Applying Particle Swarm Optimization Algorithm

نویسندگان [English]

  • Hamide Kazemi Alamooti 1
  • Abbas Afshar 2
  • Motahareh Saadatpour 3
1 M.Sc. of Environmental Eng., School of Civil Eng., Iran University of Science and Tech.
2 Prof. of Civil Eng., School of Water Eng., Iran University of Science and Tech.
3 Ph.D. Candidate of Water Quality Management, School of Civil Eng., Iran University of Science and Technology
چکیده [English]

In this study, Particle Swarm Optimization (PSO) algorithm has been used to calibrate CE-QUAL-W2 model as a water quality simulation model. PSO algorithm as an optimization technique is applied to optimize the objective function of automatic thermal calibration process.  Sum of the absolute difference between simulated results and field data in monitoring stations in Karkheh Reservoir has been considered as an objective function. With sensitivity analyzing, the most significant parameters have the most influence on the temperature profile in Karkheh Reservoir have been identified. These parameters were wind sheltering coefficient (WSC), extinction coefficient for pure water (EXH2O), solar radiation absorbed in surface layer (BETA), and empirical coefficients of wind speed function (AFW, BFW, and BFW). The efficiency of the automatic calibration model (PSO-CE_QUAL_W2) has been evaluated with the hypothetical data in Karkheh reservoir. Then the evaluated model has been applied in vertical temperature calibration in Karkheh Reservoir during 90 days. The vertical temperature profiles of the model results agree closely with the set of field data.

کلیدواژه‌ها [English]

  • CE-QUAL-W2
  • Particle Swarm Optimization
  • Calibration
  • Water Quality Simulation
1- Afshar, A., and Saadatpour, M. (2009). “Eutrophication in dam reservoirs: 2D modeling of the Karkheh reservoir.” J. of Water and Wastewater, 71, 80-93 (In Persian)
2- Mohamadi, H. (2002), “Two dimentional reservoir eutrophication modeling.” M.Sc. Thesis, Dept. of Civil Engineering, University of Science and Technology, Tehran, Iran. (In Persian)
3- Samaee, M.R., Afshar, A., and Gharavi, M. (2007). “System dynamic modeling of phytoplankton and zooplankton in resevoir.” J. of Water and Wastewater, 52, 47-55. (In Persian)
4- Jaffe, P.R., and Paniconi, C. (1988). “Model calibration based on random environmental fluctuations.” J. of Environ. Eng., 114(5), 1136-1145.
5- McCutcheon, S.C. (1989). Water quality modeling, Volume I - transport and surface exchange in rivers, CRC press, Boca Raton, Fla.
6- Cooper, V.A., Nguyen, V.T.V., and Nicell, J.A. (1997). “Evaluation of global optimization methods for conceptual rainfall-runoff model calibration.” J. of Water Sci. Tech., 36(5), 53-60.
7- Finley, J.R., Pintér, J.D., and Satish, M.G. (1998). “Automatic model calibration applying global optimization techniques.” J. of Environ. Model. and Assess., 3, 117-126.
8- Mulligan, A.E., and Brown, L.C. (1998). “Genetic algorithms for calibrating water quality models.” J. of Environ. Eng., 124(3), 202-211.
9- Shen, J., and Kuo, A.Y. (1998). “Application of inverse method to calibrate estuarine eutrophication model.” J. of Environmental Engineering, 124(5), 409-418.
10- Saadatpour, M., and Afshar, A. (2007). “Thermal calibration of water in reservoir applying genetic algorithm.” 2nd National Conf. of Water Resource Management, Isfahan University of Tech., Isfahan, Iran. (In Persian)
11- Kazemi, H. (2010). “Calibration of large scale water quality model (CE-QUAL-W2) by hybrid algorithm.” M.Sc. Thesis, Dept. of Civil Engineering, Iran University of Science and Technology, Tehran. (In Persian)
12- Ng, A.W.M., and Perera, B.J.C. (2003). “Selection of genetic algorithm operators for river water quality model calibration.” J. of Engineering Applications of Artificial Intelligence, 16, 529-541.
13- Goktas, R.K., and Aksoy, A. (2007). “Calibration and verification of QUAL2E using genetic algorithm optimization.” J. of Water Resource Plan. and Manag., 133 (2), 126-136.
14- Zakermoshfegh, M., Neyshabouri, S.A., and Lucas, C. (2008). “Automatic calibration of lumped conceptual rainfall-runoff model using particle swarm optimization.” J. of Applied Sciences, 8(20), 3703-3708.
15- Chau, K.W. (2007). “Application of a PSO-Based neural network in analysis of outcomes of construction claims.” J. of Automation in Construction, 16, 642-646.
16- Shi, Y., and Eberhart, R. (2008). Monitoring of particle swarm optimization, Higher Education Press and Springer-Verlag, New York.
17- Hassan, R., Cohanim, B., and deWeck, O. (2004). Comparison of particle swarm optimization and the genetic algorithm, American Institute of Aeronautics and Astronautics.
18- Panda, S., and Padhy, N.P. (2007). “Comparison of particle swarm optimization and genetic algorithm for TCSC-based controller design.” Int. J. of Electrical and Electronics Engineering, 1(1), 41-49
19- Ababneh, J.I., and Bataineh, M.H. (2008). “Linear phase FIR filter design using particle swarm optimization and genetic algorithms.” J. of Digital Signal Processing, 18, 657-668.
20- Yang, F., Zhang, C., and Sun, T. (2008). “Comparison of particle swarm optimization and genetic algorithm for HMM training.”19th Conf. of IEEE, Boston, USA, 1-4.
21- Meraji, S.H. (2004). “Optimum design of flood control systems by particle swarm optimization algorithm.” M.Sc. Thesis, Dept. of Civil Engineering, Iran University of Science and Technology, Tehran. (In Persian)
22- Cole, T.M., and Wells, S.A. (2003). “CE-QUAL-W2: A Two-Dimensional, laterally averaged, hydrodynamic and water quality model, version 3.2 user manual.” <http://www.ce.pdx.edu/w2/w2v3.php3>. (Oct., 2009)