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  • پیش بینی جریان آبراهه ای با استفاده از مدل های هیبریدی هوشمند در مقیاس ماهانه (مطالعه موردی: رودخانه زرین رود)

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کد مقاله : JEST-1701-3331 (R1) بازدید : 65 صفحه: 71 - 81

10.22034/jest.2020.24315.3331

نوع مقاله: پژوهشی

پیش بینی جریان آبراهه ای با استفاده از مدل های هیبریدی هوشمند در مقیاس ماهانه (مطالعه موردی: رودخانه زرین رود)

محورهای موضوعی : مدیریت منابع آب

بابک محمدی 1 (کارشناسی ارشد مهندسی منابع آب، گروه مهندسی آب ، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران* (مسوول مکاتبات))
روزبه موذن زاده 2 (استادیار گروه آب و خاک، دانشکده کشاورزی، دانشگاه صنعتی شاهرود، شاهرود، ایران)

تاریخ دریافت : 1395/11/06 تاریخ پذیرش : 1396/03/03 تاریخ انتشار : 1398/09/01

کلید واژه: شبیه سازی تبرید, تئوری آنتروپی, دبی رودخانه, الگوریتم هیبریدی, ازدحام ذرات,

چکیده مقاله :

زمینه و هدف: انتخاب ورودی‌های مناسب برای مدل‌های هوشمند از اهمیت بسزایی برخوردار است زیرا باعث کاهش هزینه و صرفه‌جویی در وقت و افزایش دقت و کارایی مدل‌ها می‌شود. هدف از پژوهش حاضر،کاربرد آنتروپی شانون برای انتخاب ترکیب بهینه متغیرهای ورودی در شبیه سازی دبی ماهانه توسط پارامترهای هواشناسی می‌باشد. روش بررسی: در این مطالعه داده های هواشناسی و سری زمانی ماهانه دبی رودخانه زرین رود (ایستگاه صفاخانه) واقع در آذربایجان- شرقی در دوره زمانی 1336تا1394 مورد استفاده قرارگرفت. پارامترهای هواشناسی و ماه از سال به‌عنوان ورودی در روش آنتروپی به منظور تعیین ترکیب موثر در نظر گرفته شد. یافته ها: نتایج آنتروپی شانون نشان داد که پارامترهای بارش ، ماه از سال و دما ، نتایج بهتری را برای مدل‌سازی ارایه می‌دهد. شبیه‌سازی با استفاده از مدل های هیبرید هوشمند الگوریتم هیبریدی ازدحام ذرات و الگوریتم هیبریدی شبیه سازی تبرید انجام گرفت.کارایی مدل‌ها با استفاده از معیار ضریب تبیین ،ریشه جذر میانگین خطا وشاخص پراکندگی محاسبه گردید. بحث و نتیجه گیری: نتایج نشان داد از میان این مدل ها با ساختار ورودی‌های یکسان، مدل الگوریتم هیبریدی شبیه سازی تبرید بر پایه ماشین بردار پشتیبان عملکرد بهتری برای شبیه‌سازی دبی جریان در مقایسه با سایر مدل های هیبریدی هوشمند داشته است. همچنین نتایج تحقیق نشان داد که روش آنتروپی در انتخاب بهترین ترکیب ورودی در مدل‌های هوشمند از کارایی خوبی برخوردار است.

چکیده انگلیسی:

Background and Objective: Selecting appropriate inputs for intelligent models are important because it reduces the cost and saves time and increases accuracy and efficiency of its models. The aim of the present study is the use of Shannon entropy to select the optimum combination of input variables in the simulation of monthly flow by meteorological parameters. Method: In this study, meteorological data and monthly time series of discharge of Zarrinrood River (Safavankeh Station) in East Azarbaijan from 1336 to 2015 were used. The meteorological parameters and the month of the year were considered as inputs in the entropy method to determine the effective composition. Results: Shannon entropy results showed that the rainfall parameters, month of year and temperature provide better results for modeling. The simulations were performed using intelligent hybrid models of particle swarm hybrid algorithm and hybrid simulation hybrid algorithm. Discussion and Conclusion: The results showed that among these models with the same input structure, the hybrid algorithm simulation based on the support vector machine had better performance for simulating the flow rate compared to other intelligent hybrid models. The results also show that the entropy method is good for selecting the best input combination in smart models.

منابع و مأخذ:


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_||_

  1. Farajzadeh, J., FakheriFard, A. and Lotfi, S. 2014. Modeling of monthly rainfall and runoff of Urmia lake basin using feed-forward neural network and time series analysis model. Water Resources and     Industry.7 (8).38-48.
    2.

  1. Hassan, R. Cohanim, B. Weck, O. 2004. A copmarison of particle swarm optimization and the genetic algorithm. American Institute of Aeronautics and Astronautics. 4(1).12-33.
    2.
  2. Chau, K. 2006. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of hydrology 329(3): 363-367
    3.
  3. Chau, K. 2007. A split-step particle swarm optimization algorithm in river stage forecasting. Journal of hydrology, 346(3): 131-135.
    4.
  4. Harmancioglu, N. B. and N. Alpaslan. 1992. Water quality monitoring network design: A problem of multi-objective decision making. Water Resour. Bull. 28(1): 179-192.
    5.
  5. Guey-Shin S, Bai-You C, Chi TC, Pei HY.Tsun KC. 2011. Applying Factor Analysis Combined with Kriging and Information Entropy Theory for Mapping and Evaluating the Stability of Groundwater Quality Variation in Taiwan. International Journal Environmental Resources Public Health, 8: 1084-1109
    6.
  6. Singh, V. P. and K. Singh. 1985. Derivation of the Pearson type (PT)-III distribution by using the principle of maximum entropy (POME). J. Hydrol. 80: 197–214.
    7.
  7. Misra, D.  T., Oommen.  A., Agarwal.  A., Mishra. S.K.   2009. Application and analysis of Support Vector machine based simulation for runoff and sediment yiel. Biosystems Engineering, 103, 527-535.
    8.
  8. Carmona,  G., Molina. J.L., Bromley.  J., Varela-Ortega.  C., Garcia-Arostegu. J.L., 2011. Object Orientedbayesian network for participatory water management: Two case Studise in Spain.  Jornal of Water resources planning and managemen, 137, 366-376.
    9.
  9. Karamouz, M., A. K. Nokhandan, R. Kerachian and C. Maksimovic. 2009. Design of on-line river water quality monitoring systems using the entropy theory: a casestudy. Environ. Monit.Assess. 155(1-4): 63-81.
    10.
  10. Karimi Hoeesini,A.,2009.Compare the methods of locating the rain-gauge stations in the GIS environment.
    11.
  11. Chen, sh., 2015.  Mining Informative Hydrologic Data by Using Support Vector Machines and Elucidating Mined Data according to Infoarmation Entropy, 17, 1023 – 1041.
    12.
  12. Amorocho, J. and B. Espildora. 1973. Entropy in the assessment of uncertainty in hydrologic systems and models. Water Resour. Res. 9(6): 1551-1522.
    13.
  13. 14- Chiang ,W , Hui-Chung, Y. 2014. Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy. Entropy, 16 , 4626-4647.
    14.
  14. Remesan, R. Shamim, M.A. and Han, D. 2008. Model data selection using gamma test for daily solar radiation estimation. Hydrological Processes, 22: 4301-4309.
    15.
  15. Harmancioglu, N. B. 1984. Entropy concept as used in determination of optimum sampling intervals. Proc. of Hydrosoft 84, International Conf. on Hydraulic Engineering Software, September 10-14, 1984. Portoroz, Yugoslavia, pp. 6-99 and 6-110.
    16.
  16. Kennedy, J. Russell, E 2011. Particle swarm optimization. Encyclopedia of machine learning, Springer: 760-766
    17.
  17. Dawson, C.W., Abrahart, R.J., Shamseldin, A.Y. and R.L. Wibly. 2006, Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology. 319 (1-4): 391-409.           
    18.

  1. Chen, sh.  2016. Apllication Hydrologic Data Mining Using Articiial Nerual Network, Entropy. 12, 83 –98.
    2.
  2. Masoumi, F., Karachian, R., 1387. Evaluating the efficiency of groundwater quality monitoring systems      using    discrete entropy theory. Case Study: Tehran Aquifer, Water and Wastewater Journal, Volume 19,     Issue 1, 12-2 (In Persian).
    3.

  1. Gonzalez R. C. and Perez V. S., (2001). Two procedures for stochastic simulation of vuggy formations, SPE 69663, Latin American and Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, pp. 25–28 March.
    2.
  2. Tran N. H. and Tran K., 2007. Combination of fuzzy ranking and simulated annealing to improve discrete           fracture inversion Elsevier”, Mathematical and Computer Modeling, Vol. 45, pp. 1010–    1020.
    3.
  3. Fabian V., 1997. Simulated annealing simulated computers & mathematics with applications, Vol. 33, No.          1/2, pp.81-94.
    4.
  4. Pai, PF.; WC. Hong. 2007. A recurrent support vector regression model in rainfall forecasting. Hydrological Process, 21:819-827.
    5.
  5. Dibike, Y., Velickov, S., Solomatine, D., Abbott, M., 2001. Model induction with of support vector   machines: Introduction and applications. Journal of Computing in Civil Engineering, Vol. 15, PP.          208- 216.
    6.

  1. Coulibaly, P., Anctil, F., Bobée, B., 2000. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology, Vol. 230, PP. 244-257.
    2.
  2. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000. Artificial           neural networks in hydrology. I preliminary concepts. Journal of Hydrologic Engineering, Vol.5,             PP.115-123.
    3.
  3. Shannon CE, Weaver W. 1949. The Mathematical Theory of Communication. University of Illinois Press:            Urbana, IL.
    4.

 



 

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