IJE TRANSACTIONS C: Aspects Vol. 27, No. 6 (June 2014) 855-864   

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M. Sadeghpour Haji, S. A. Mirbagheri, A. H. Javid, M. Khezri and G. D. Najafpour
( Received: September 14, 2013 – Accepted: December 12, 2013 )

Abstract    Abstract In this study, wavelet support vector machine (WSWM) model is proposed for daily suspended sediment (SS) prediction. The WSVM model is achieved by combination of two methods; discrete wavelet analysis and support vector machine (SVM). The developed model was compared with single SVM. Daily discharge (Q) and SS data from Yadkin River at Yadkin College, NC station in the USA were used. In order to evaluate the model, the root mean square error (RMSE), correlation coefficient (R) and coefficient of determination (R2) were used. Results demonstrated that WSVM with RMSE =3294.6, R =0.9211 and R2 =0.838 were more desired than the other model with RMSE =6719.7, R=0.589 and R2=0.327. Comparisons of these models revealed that, mean of error and error standard deviation for WSVM model were about 66% and 50% less than SVM model in test period.


Keywords    Discrete wavelet analysis, Support vector machine, Daily discharge, Suspended sediment


چکیده    چكيده: در این تحقیق از ترکیب تئوری موجک با ماشین بردار پشتیبان استفاده شده است. این مدل توسعه یافته با ماشین بردار پشتیبان مقایسه گردید. از دیتاهای رودخانه یادکین درآمریکا استفاده گردید و گام‌های گذشته دبی و رسوب و ترکیب آنها به عنوان ورودی به مدل انتقال داده شد. از برخی شاخص‌های آماری نظیر ضریب همبستگی (R)و ضربیب تبیین (R2) و جذر میانگین مربعات خطا ( (RMSEبرای ارزیابی مدل استفاده گردید. نتایج نشان داد که ترکیب تئوری موجک با ماشین بردار پشتیبان دارای نتایج بهتری باRMSE =3294.6, R=0.9211 و R2 =0.838 نسبت به ماشین بردار پشتیبان به تنهایی با RMSE =6719.7, R R=0.589 وR2 =0.327 می باشد. میانگین و انحراف معیار خطا نیزدر مدل موجک - ماشین بردار پشتیبان نسبت به ماشین بردارپشتیبان 66% و 50% کاهش نشان داد.



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