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Daneshfaraz, Rasoul, Aminvash, Ehsan, Mirzaee, Reza, Abraham, John. (1400). Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm. , 8(2), 98-106. doi: 10.22126/arww.2021.5886.1195
Rasoul Daneshfaraz; Ehsan Aminvash; Reza Mirzaee; John Abraham. "Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm". , 8, 2, 1400, 98-106. doi: 10.22126/arww.2021.5886.1195
Daneshfaraz, Rasoul, Aminvash, Ehsan, Mirzaee, Reza, Abraham, John. (1400). 'Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm', , 8(2), pp. 98-106. doi: 10.22126/arww.2021.5886.1195
Daneshfaraz, Rasoul, Aminvash, Ehsan, Mirzaee, Reza, Abraham, John. Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm. , 1400; 8(2): 98-106. doi: 10.22126/arww.2021.5886.1195

Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm

Journal of Applied Research in Water and Wastewater
مقاله 2، دوره 8، شماره 2 - شماره پیاپی 16، اسفند 2021، صفحه 98-106    اصل مقاله (1.47 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22126/arww.2021.5886.1195
نویسندگان
Rasoul Daneshfaraz* 1؛ Ehsan Aminvash2؛ Reza Mirzaee3؛ John Abraham4
1Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran.
21Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran.
3Department of Civil Engineering, Faculty of Engineering, University of Semnan, Semnan, Iran.
4School of Engineering, Faculty of Engineering, University of St. Thomas, St Paul, USA.
چکیده
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.
کلیدواژه‌ها
Relative energy dissipation؛ Support vector machine؛ Input parameters؛ Froude number
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