2024 : 11 : 23
Rasoul Daneshfaraz

Rasoul Daneshfaraz

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId:
HIndex:
Faculty: 1
Address:
Phone:

Research

Title
Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm
Type
JournalPaper
Keywords
Relative energy dissipation Support vector machine Input parameters Froude number
Year
2021
Journal Journal of Applied Research in Water and Wastewater
DOI
Researchers Rasoul Daneshfaraz ، Ehsan Aminvash ، Reza MIRZAEE ، JOHN ABRAHAM

Abstract

In this research, the performance of support vector machine in predicting relative energy dissipation in non-prismatic channel and rough bed with trapezoidal elements has been investigated. To achieve the objectives of the present study, 136 series of laboratory data are analyzed under the same laboratory conditions using a support vector machine. The present study entered the support vector machine network without dimension in two different scenarios with a height of 1.50 and 3.0 cm rough elements. Two statistical criteria of Root Mean Square Error and coefficient of determination are used to evaluate the efficiency of input compounds. Hydraulically, the results show that at both heights of the rough elements, energy dissipation increased with increasing Froude number. The results of the support vector machine show that the height of the roughness element is 1.50 cm in the first scenario, combination number 6 with R2 = 0.990 and RMSE = 0.0129 for training mode and R2 = 0.993 and RMSE = 0.032 for testing mode and the height of the roughness element 3.0 in the second scenario, combination number 6 with R2 = 0.989 and RMSE = 0.0112 for training mode, R2 = 0.994 and RMSE = 0.0224 for testing mode are select as the best models. Finally, sensitivity analysis is performed on the parameters and H / y1 parameter is selected as the most effective parameter.