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



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.


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

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