A Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting
Publish Year: 1393
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJE-27-6_004
تاریخ نمایه سازی: 17 خرداد 1393
Abstract:
In this study, wavelet support vector machine (WSWM) model is proposed for daily suspended sediment (SS) prediction. The WSVM model is achieved through combination of two methods; discrete waveletanalysis and support vector machine (SVM). The developed model was compared with single SVM. Dailydischarge (Q) and SS data from YadkinRiver at Yadkin College, NC station in the USA were used. In order to evaluate the model, the root mean square error (RMSE), mean absolute error(MAE) and coefficient of determination (R2) were used.Results demonstrated that WSVM with RMSE =3294.6ton/day, MAE=795.22 ton/day and R2 =0.838 were more desired than the other model with RMSE =6719.7 ton/day, ton/day and R2=0.327. Comparisons of these models revealed that, MAE and error standard deviation for WSVM model were about 40% and 50% less than SVM model in test period
Authors
m SadeghpourHaji
Department of Environmental Engineering, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran
s.a Mirbagheri
Department of Civil and Environmental Engineering, K. N. Toosi University of Technology, Tehran, Iran
a.h Javid
Islamic Azad university Tehran Science and Research Branch, Faculty of Marine Science and Technology, Tehran, Iran
m Khezri
Department of Environmental Engineering, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran