A Comparative Study of Extreme Learning Machines and Support Vector Machines in Prediction of Sediment Transport in Open Channels
محل انتشار: ماهنامه بین المللی مهندسی، دوره: 29، شماره: 11
سال انتشار: 1395
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 394
فایل این مقاله در 8 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJE-29-11_003
تاریخ نمایه سازی: 9 خرداد 1396
چکیده 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
کلیدواژه ها:
Extreme Learning Machines (ELM) ، Non-deposition ، Open channel ، Sediment transport ، Support Vector Machines (SVM)
نویسندگان
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