استفاده از مدل تبرید تدریجی عصبی (NDE) در تخمین بار معلق رسوبی و مقایسه‌ی آن با مدل ANFIS و RBF مطالعه موردی: رودخانه گیوی‌چای

نوع مقاله : مقاله پژوهشی

نویسندگان

چکیده

در این تحقیق، مدل تبرید تدریجی عصبی (NDE)با بهره‌گیری از ترکیب‌های ورودی مختلف برای تخمین بار معلق رسوبی روزانه به کار گرفته شد. به این منظور در اولین بخش از تحقیق، مدل NDEبا استفاده از داده‌های دبی روزانه و بار معلق رسوبی روزهای پیشین تعلیم داده شده و برای تخمین بار معلق رسوبی رودخانه گیوی‌چای مورد استفاده قرار گرفت. در دومین بخش از تحقیق، مدل NDE با استفاده از پارامترهای ضریب تبیین (R2) و خطای مجذور میانگین مربعات (RMSE )با مدل‌های سیستم استنتاجی فازی عصبی (ANFIS)و تابع پایه شعاعی (RBF) مقایسه گردید. نتایج نشان داد که مدل NDE با برخورداری از مقادیر ضریب تبیین (R2) معادل9586/0 و RMSE معادل 160 میلی‌گرم در لیتر در مقایسه با سایر مدل‌ها از قابلیت بهتری در تخمین بار معلق رسوبی برخوردار است. در تخمین حداکثر بار معلق رسوبی نیز مدل NDE، با برخورداری از مقادیر خطای نسبی (RE) معادل 47- درصد به نتایج بهتری دست یافته است.

کلیدواژه‌ها

عنوان مقاله [English]

Estimating Suspended Sediment Concentration by a Neaural Differential Evolution and Comparision it with ANFIS and RBF Models (Case study : Givi Chay River )

نویسندگان [English]

  • Masoume Rajabi
  • Mehdi Feyzolahpour
  • Shahram Roostaiee
چکیده [English]

In this study, neural differential evolution (NDE) models were used to estimate suspended sediment concentration. NDE models are improved by combining two methods, neural networks and differential evolution. At the first part of the study, the neural differential evolution is trained using daily river flow and suspended sediment data belonging to Givi Chay River at the  northwest of Iran and various combinations of current daily stream flows, past daily stream flows and suspended sediment data are used as inputs to the neural differential evolution model so as to estimate current suspended sediment. In the second part of the study, the suspended sediment estimations provided by NDE model are compared with adaptive neuro- fuzzy inference system (ANFIS) and radial basis function (RBF) results. The Root mean squared error (RMSE) and the determination coefficient (R2) are used as comparison criteria. Obtained results demonstrate that NDE and ANFIS are in good agreement with the observed suspended sediment concentration; while they depict better results than RBF methods. For example, in Givi Chay River station, the determination coefficient (R2) is 0.9586 for NDE model, while it is 0.9152 and 0.8872 for ANFIS and RBF models, respectively. However, for the estimation of maximum sediment peak, the NDE was mostly found to be better than the ANFIS and the other techniques. The results also indicate that the NDE may provided better performance than the ANFIS and RBF in the estimation of the total sediment load (Re= -47%).
 

کلیدواژه‌ها [English]

  • Neural differential evolution
  • Multi-layer Perceptron model
  • Generalized regression neural network
  • Sediment rating curves
  • Givi chay river

مراجع

-  دهقانی، امیراحمد؛ محمدابراهیم زنگانه؛ ابوالفضل مساعدی؛ نسرین کوهستانی (1388). مقایسه تخمین بار معلق به دو روش منحنی سنجه رسوب و شبکه‌ی عصبی مصنوعی، علوم کشاورزی و منابع طبیعی 16. صفحات 51- 36.##
-  دهقانی،امیراحمد؛محمدملک‌محمدی؛‌ابوطالب هزار‌جریبی (1389). تخمین رسوب معلق رودخانه بهشت‌آباد با استفاده از شبکه عصبی مصنوعی، پژوهش‌های حفاظت آب و خاک. 17. صفحات 168-159.##
-   Akaike, H (1974). A new look at the statistical mmodel identification. IEEE Trains. Automat. Control 19(6), 716-723.##
-   Bathurst, J.c (1996). SHESED: a physically based, distributed erosion and sediment yield component for the SHE hydrological modeling system. J. Hydrol. 175, 213-238.##
-   Bhattacharya B, price R, Solomatine D (2005). Data- driven modeling in the context of sediment transport. Phys chem. Earth 30: 297-302.##
-   Cigizoglu, H.K. & Kisi, O (2005). Flow prediction by three back propagation techniques using k- fold partitioning of neural network training data. Nordic Hyrol. 36(1): 1-16.##
-   Cigizoglu, H. K., Kisi, O (2006). Methods to improve the neural network performance in suspended sediment estimation. Journal of hydrology 317, 221- 238.##
-   Gautam, M.R., K. Watanabe and H. Saegusa (2000). Runoff analysis in humid forest catchment with artificial neural network. Journal of Hydrology, 235(1-2): 117-136.##
-   Goh, A.T.C (1995). Back-propagation neural networks for modeling complex systems. Artificial intelligence in Engineering 9, 143-151.##
-   Guldal, V. & Muftuglu, R.F (2001). 2D unit sediment graph theory. J. Hydrol. Engng 6(2),
132-140.##
-   Jain, S.K (2001). Development of integrated sediment rating curves using ANNs. Journal of hydraulic Engineering, ASCE 127(1), 30-37.##
-   Jang, J (1993). ANFIS: adaptive- network-based fuzzy inference system. IEEE Transactions on systems, Man and cybernetics 23(3), 665-685.##
-   Karterakis, S.M., Karatzas, g.P., nikolos, I.K., Papadopoulou, M.P (2007). Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria. J. Hydrol. 342(3-4), 270-282.##
-   Kisi, O (2004). Multi- layer perceptrons with Levenberg- marquardt optimization algorithm for suspended sediment concentration prediction and estimation. Hydrological Sciences Journal 49(6), 1025-1040.##
-   Kisi, O (2005). Suspended sediment estimation using neuro- fuzzy and neural network approaches. Hydrological Sciences Journal 50(4), 683-696.##
-   Kisi, O., Karahan, M.E., Sen, Z (2006). River suspended sediment modeling using fuzzy logic approach. Hydrological processes 20(20), 4351- 4362.##
-   Kisi, O, Yuksel, I., Dogan, E (2008). Modeling daily suspended sediment of rivers in Turkey using several data driven techniques. Hydrol. Sci. J. 53(6), 1270-1285.##
-   Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., Pruitt, W.O (2002). Estimating evapotranspiration using artificial neural network. J. Irrig. Drain. Eng. 128(4), 224-233.##
-   Lampinen, J, Zelinka, I (2000). On stagnation of the differential evolution algorithm. In: Osmera, Pavel(Ed), proceedings of MENDEL 200, 6th international mendel conference on soft computing, June 7-9 2000, Brno, Czech Republic. Brno university of Technology, Faculty of Mechanical Engineering, Institute of Automation and computer science, Brno, Czech Republic, PP. 76-83.##
-   Lampinen, J (2001). Solving problems subject to multiple non linear constraints by the differential evolution. In: proceeding of MENDEL 2001, 7th international conference on soft computing, June 6-8 2001, brno, Czech Republic, Brno university of technology, faculty of Mechanical engineering, Institute of Automation and computer science, brno, Czech republic, PP. 50-57.  ISBN 80-214-1894-x.##
-   Legates DR, Mc Cabe JrGJ (1999). Evaluating the use of goodness- of- fit measures in hydrologic and hydro climatic model validation. Water resour Res. 35(1): 233-41.##
 
 
-   Lohani, a.K., Goel, N.K., Bhatia, K.K.S (2007). Deriving Stage- discharge- sediment concentration relationships using fuzzy logic. Hydrological sciences Journal 52(4), 793-807.##
-   Mc Bean, E.A., Al- Nassri, S (1988). Uncertainty in suspended sediment transport curves, Journal of hydrologic Engineering ASCE 114(1), 63-74.##
-   Mantoglu,A.,Papuntoniou, M., Giannoulopoulos, P (2004). Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms. J. Hydrol. 297(1-4), 209-228.##
-   Masters, T (1993). Practical Neural network Recipes C++. Academic press, San Diego, California, USA. pp: 498.##
-   Nakato, T (1990). Test of selected sediment- transport Formulas.J. of hydro. Engrg., ASCE, 116(3), 362-379.##
-   Nash JE, Sutcliff JV (1970). River flow forecasting through conceptual models part I-a discussion of principles. J hydrol: 10(3): 282-90.##
-   Ocampo- Duque W, Schuhmacher M, Domingo JL (2007). A neural fuzzy approach to classify the ecological status in surface waters. Environ pollut. 148:634-41.##
-   Ozturk, F., Apaydin, H., & Walling, D.E (2001). Suspended sediment loads through Flood events for streams of sakarya Basin. Turkish J. Eng. Env. TUBITAK 25, 643-650.##
-   Raghuwanshi N, Singh R, Reddy L (2006). Runoff and sediment yield modeling using artificial neural networks: upper Siwane River, India. J Hydrol Eng; 11(1): 71-9.##
-   Refsgaard,J.c(1997).parameterization, calibration and validation of distributed hydrological models. J. Hydrol. 198, 69-97.##
-   Storn, R., Price, K.V (1995). Differential Evolution- A simple and Efficient Adaptive scheme for Global optimization over continuous spaces, Technical Report, TR-95-012, ICSI, March. PP: 375.##
-   Storn, R., Price, K.V (1997). Differential evolution- a simple and efficient heuristic for global optimization and continuouse space. J. Global optim. 11(4), 341-359.##
 
 
 
 
 
 
-   Sudheer, K.P., Gosain, A.K., Ramasastri, K.S (2003). Estimating actual evapotranspiration from limited climatic data, using neural computing technique.J.Irrig.Drain. Eng, ASCE 129(3),214-218.##
-   Tayfur, G (2002). Artificial neural networks for sheet sediment transport.Hydrol.Sci.J.47(6),879- 892.##
-   Tayfur, G., Ozdemir, S., Singh, V. P (2003). Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Advances in water Resource 26(12), 1249-1256.##
-   Tayfur, G., Guldal, V., 2006. Artificial neural networks for estimating daily total suspended sediment in natural streams, Nordic Hydrology 37, 69-79.##
-   Zounemat-Kermani M, Teshnehlab M (2008). Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl soft comput; 928-36.##
-   Zounemat-Kermani M, Beheshti AA, Ataie- Ashtiani B,Sabbagh-Yazd SR (2009). Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Appl soft comput. 9: 746-55.##

فایل ها

سابقه مقاله

  • تاریخ دریافت: 18 خرداد 1394
  • تاریخ پذیرش: 18 خرداد 1394

اشتراک گذاری

ارجاع به این مقاله

آمار