مدلسازی رابطه دبی - اشل در رودخانه‌ها با استفاده از سیستم‌های هوشمند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه تبریز

2 دانشگاه بوعلی سینا همدان

چکیده

اندازه­گیری دائمی دبی رودخانه­ها در ایستگاههای هیدرومتری مخصوصاً در مواقع سیلابی غالباً پر هزینه و مشکل می‌باشد، بدِِین منظور جهت مدل­سازی رابطه دبی- اشل مدل­های ریاضی و هوشمند متعددی توسعه یافته و مورد استفاده قرار می­گیرد. در این تحقیق از سه روش شبکه­های عصبی مصنوعی ، سیستم استنتاج عصبی- فازی و برنامه‌ریزی ژنتیک برای مدل­سازی رابطه دبی-اشل روزانه در دو ایستگاه یامولا و سوقوتلوهان واقع در رودخانه قیزیلیرماک کشور ترکیه استفاده شده است. مدل­سازی رابطه دبی- اشل با سه روش مذکور با ترکیب­های مختلف ورودی شامل اشل­ها و دبی انجام شد که با توجه به نتایج ارزیابی­، روش سیستم استنتاج عصبی- فازی از دقت بیشتری برخوردار است.  بدلیل  قابلیت استخراج رابطه ریاضی و انتخاب متغیرهای معنی­دار توسط روش برنامه­ریزی ژنتیک، مدل حاصل از این روش با چهار عمل اصلی{+,-,*,/}وبا ضریب تعیین، ریشه میانگین مربعات خطا و قدرمطلق میانگین خطا در ایستگاه یامولا به ترتیب 000/1، 974/0، 552/0 و درایستگاه سوقوتلوهان 999/0، 095/1 و 630/0 به عنوان بهترین مدل دبی-اشل انتخاب گردید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Stage -Discharge Relationship Modeling in Rivers Using Intelligent Systems

نویسندگان [English]

  • A Soltani 1
  • E Olyaie 2
  • MA Ghorbani 1
چکیده [English]

Since continual measurement of rivers discharge, specialy at  flooding time is expensive and difficult task, modeling of stage-discharge relationship using mathematical and intelligent models have been  developed and used. In this research artificial neural networks, inference neuro-fuzzy and genetic programming were used for modeling daily stage- discharge relationship at the two stations, Yamula and Sogutluhan on the Kizilirmak  river in Turkey. Modeling were accomplished by the above three methods with various  combination of inputs  including the previous stage-discharge data. According to the results obtained,  neuro-fuzzy showed greater accuracy. Due to ability of genetic programming for evolving mathematical relationship and selecting significant variables, this method was selected as the best for stage-discharge relationship modeling  using four main operators including {+,-,*,/} with R2,RMSE and MAE for Yamula station are 1.000, 0.974 and 0.552 and for Sogutluhan station, 0.999, 1.095 and 0.630 respectively. 

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • genetic programming
  • Modeling
  • Inference neuro-fuzzy system
  • Sogutluhan
  • Stage-Discharge
  • Yamula

مراجع

Abreu GL and Ribeiro J, 2003. On-line control of a flexible beam using adaptive fuzzy controller and piezoelectric actuators. Revista Controle & Automação. 14(4): 377-383.
Alvisi S, Mascellani G, Franchini M and Bardossy A, 2005. Water level forecasting through fuzzy logic and artificial neural network approaches. J Hydrol System Science 2: 1107-1145.
Aytek A and Kisi O, 2008. A genetic programming approach to suspended sediment modeling. J Hydrol 351: 288-298.
Aytek A, Asce M and Alp H, 2008. An application of artificial intelligence for rainfall runoff modeling. J Earth System Science 117(2): 145-155.
Bhattacharya B and Solomatine DP, 2000. Application of artificial neural network in stage-discharge relationships, 4th Int. Conf. on Hydroinformatics. Cedar-Rapids, USA.
Chang FJ and ChangYT, 2006. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advanced Water Research 29: 1–10.
Chibanga R, Berlamont J and Vandewalle J, 2003. Modeling and forecasting of hydrological variables using artificial neural networks: the KafueRiver sub-basin. Hydrol Sci J 48(3): 363 – 379.
Clair TA and Ehrman JM, 1998. Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resour Res 34(3): 447– 455.
Cigizoglu HK, 2003. Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol Sci J 48(3): 349 – 361.
Cigizoglu HK and Kisi O, 2005. Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrol 36(1): 49–64.
Coulibaly P, Anctil F and Bobe'e B, 1998. Real time neural network based forecasting system for hydropower reservoirs, pp. 1001 – 1011. In: Proc. of the First Int. Conf. on New Information Technologies for Decision Making in Civil Engineering (Ed: ET Miresco). University of Quebec, Montreal, Canada, October.
Coulibaly P, Hache M, Fortin V and Bobe'e B, 2005. Improving daily reservoir inflow forecasting with model combination, J Hydrol Eng, ASCE 10(2): 91 – 99.
Deka P and Chandramouli V, 2003. A fuzzy neural network model for deriving the river stage-discharge relationship. Hydrol Sci J 48(2): 197 – 209.
JainSK, Das D and Srivastava DK, 1999. Application of ANN for reservoir inflow prediction and operation. J Water Resour Planning Manag, ASCE 125(5): 263 – 271.
JainSK and Chalisgaonkar D, 2000. Setting up stage discharge relations using ANN. J Hydrol Eng, ASCE 5 (4): 428– 433.
Jang JSR, 1993. ANFIS: adaptive network based fuzzy inference systems. IEEE Transactions on Systems, Man and Cybernetics 23(3): 665–685.
Khu ST, Liong SY, Babovic V, Madsen H and Muttil N, 2001. Genetic programming and its application in real- time runoff forming. J Am Water Res Assoc 37(2): 439-451.
Kisi O, 2004. River flow modeling using artificial neural networks. J Hydrol Eng, ASCE 9(1): 60 – 63.
Kisi O, 2005. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Science Journal. 50(4): 683–696.
Kisi O, 2006. Daily pan evaporation modelling using a neuro-fuzzy computing technique. Journal of Hydrology 329: 636–646.
Kisi O and Cobaner M, 2009. Modeling river stage-discharge relationship using different neural network computing techniques. Clean 37(2): 160 – 169.
Koza JR, 1992. Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA, MIT Press.
Lin GF and Chen LH, 2004. A nonlinear rainfall-runoff model using radial basis function network. J Hydrol 289: 1–8.
Liong SY, Gautam TR, Khu ST, Babovic V, Keijzer M and Muttil N, 2002. Genetic programming: A new paradigm in rainfall runoff modeling". J Am Water Res Assoc 38(3): 705-718.
Nayak PC, Sudheer KP, Rangan DM and RamasastriKS, 2004. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology 291(1–2): 52–66.
Raman H  and Sunilkumar N, 1995. Multivariate modeling of water resources time series using artificial neural networks. Hydrol Sci J 40 (2): 145 – 163.
Sette S and Boullart L, 2001. Genetic programming: principles and applications. Engineering Applications of Artificial Intelligence. 14: 727–736.
Solomatine DP and Dulal KN, 2003, Model trees as an alternative to neural networks in rainfall-runoff modeling. Hydrol Sci J 48 (3): 399 –411.
Smith J and Eli RN, 1995. Neural network models of rainfall-runoff process, J Water Resour Planning Manag  6(121): 499–508.
TokarAS and Johnson PA, 1999. Rainfall-runoff modeling using artificial neural networks. J Hydrol  Eng, ASCE 4(3): 232 – 239.
Ustoorikar K and Deo MC, 2008. Filling up gaps in wave data with genetic programming. Marine Structures 21: 177-195.
 

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