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گواهی نمایه سازی مقاله A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels

عنوان مقاله: A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
شناسه (COI) مقاله: JR_IJE-29-11_003
منتشر شده در ماهنامه بین المللی مهندسی در سال ۱۳۹۵
مشخصات نویسندگان مقاله:

I. Ebtehaj - Department of Civil Engineering, Razi University, Kermanshah, Iran. Water and Wastewater Research Center, Razi University, Kermanshah, Iran
H Bonakdari - Department of Civil Engineering, Razi University, Kermanshah, Iran. Water and Wastewater Research Center, Razi University, Kermanshah, Iran

خلاصه مقاله:
The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicted using ELM and the results are compared to those obtained using a Support Vector Machines (SVM). The comparison of the ELM and SVM methods indicates a good performance for both methods in the prediction of Fr. In addition to being computationally faster, the ELM method has a higher level of accuracy (R2=0.99, MAE=0.10; MAPE=2.34; RMSE=0.14; CRM=0.02) compared with the SVM approach

کلمات کلیدی:
Extreme Learning Machines (ELM),Non-deposition,Open channel,Sediment transport,Support Vector Machines (SVM)

صفحه اختصاصی مقاله و دریافت فایل کامل: https://www.civilica.com/Paper-JR_IJE-JR_IJE-29-11_003.html