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Suspended sediment prediction using wavelet with RBF-ANN and SVM

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Linnaeus ECO-TECH 2016 Kalmar, Sweden, November 21-23, 2016

223

SUSPENDED SEDIMENT PREDICTION USING

WAVELET WITH RBF-ANN AND SVM

Maedeh Sadeghpour Haji

1

Ghasem Najafpur

2

Nastaran Azimi

3

1

Islamic Azad University

2

Noshirvani University of Technology

3

Islamic Azad University

Iran

Abstract

In this study wavelet radial basis function artificial neural network (WRBF-ANN) and wavelet support vector machine (WSVM) model is proposed for daily suspended sediment (SS) prediction in river. These models are achieved by combining discrete wavelet analysis with support vector machine (SVM) and radial basis function artificial neural network (RBF-ANN). Daily discharge (Q) and SS data from Yadkin River in the USA are used. The root mean square error (RMSE), correlation coefficient (R) and coefficient of efficiency (

R

2) are used to evaluate the models. Results demonstrated that WRBF-ANN with RMSE =2167.98 ton/day and R2 =0.91 were more desired than the other model with RMSE =3294.61 ton/day and R2=0.838. Comparisons of these models revealed that, RMSE and error standard deviation for WRBF-ANN model were about 0.34% less than WSVM model in test period.

Keywords

Discrete wavelet analysis; RBF-artificial neural network; Daily discharge; Suspended sediment; Support vector machine

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