SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop
The present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the d...
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doaj-d7addc8c7d8d44d7a5d25628d21c4c782021-05-31T23:23:20ZengMDPI AGApplied Sciences2076-34172021-05-01114238423810.3390/app11094238SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical DropRasoul Daneshfaraz0Ehsan Aminvash1Amir Ghaderi2John Abraham3Mohammad Bagherzadeh4Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh 8311155181, IranDepartment of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh 8311155181, IranDepartment of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan 537138791, IranSchool of Engineering, University of St. Thomas, St. Paul, MN 55105, USADepartment of Civil Engineering, Faculty of engineering, Urmia University, Urmia 5756151818, IranThe present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the destructive energy of the flow, cause turbulence. The turbulence in turn supplies oxygen to the system through the promotion of air–water mixing. To achieve the objectives of the present study, 164 experiments were analyzed under the same experimental conditions using a support vector machine. The approach utilized dimensionless terms that included scenario 1: the relative energy consumption and scenario 2: the relative pool depth. The performance of the models was evaluated with statistical criteria (RMSE, R<sup>2</sup> and KGE) and the best model was introduced for each of the parameters. RMSE is the root mean square error, R<sup>2</sup> is the correlation coefficient and KGE is the Kling–Gupta criterion. The results of the support vector machine showed that for the first scenario, the third combination with R<sup>2</sup> = 0.991, RMSE = 0.00565 and KGE = 0.998 for the training mode and R<sup>2</sup> = 0.991, RMSE = 0.00489 and KGE = 0.991 for the testing mode were optimal. For the second scenario, the third combination with R<sup>2</sup> = 0.988, RMSE = 0.0395 and KGE = 0.998 for the training mode and R<sup>2</sup> = 0.988, RMSE = 0.0389 and KGE = 0.993 for the testing mode were selected. Finally, a sensitivity analysis was performed that showed that the y<sub>c</sub>/H and D/H parameters are the most effective parameters for predicting relative energy dissipation and relative pool depth, respectively.https://www.mdpi.com/2076-3417/11/9/4238relative energy dissipationrelative pool depthsupport vector machinevertical drophorizontal screen |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rasoul Daneshfaraz Ehsan Aminvash Amir Ghaderi John Abraham Mohammad Bagherzadeh |
spellingShingle |
Rasoul Daneshfaraz Ehsan Aminvash Amir Ghaderi John Abraham Mohammad Bagherzadeh SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop Applied Sciences relative energy dissipation relative pool depth support vector machine vertical drop horizontal screen |
author_facet |
Rasoul Daneshfaraz Ehsan Aminvash Amir Ghaderi John Abraham Mohammad Bagherzadeh |
author_sort |
Rasoul Daneshfaraz |
title |
SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop |
title_short |
SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop |
title_full |
SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop |
title_fullStr |
SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop |
title_full_unstemmed |
SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop |
title_sort |
svm performance for predicting the effect of horizontal screen diameters on the hydraulic parameters of a vertical drop |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-05-01 |
description |
The present study investigated the application of support vector machine algorithms for predicting hydraulic parameters of a vertical drop equipped with horizontal screens. The study incorporated varying sizes of a rectangular channel. Horizontal screens, in addition to being able to dissipate the destructive energy of the flow, cause turbulence. The turbulence in turn supplies oxygen to the system through the promotion of air–water mixing. To achieve the objectives of the present study, 164 experiments were analyzed under the same experimental conditions using a support vector machine. The approach utilized dimensionless terms that included scenario 1: the relative energy consumption and scenario 2: the relative pool depth. The performance of the models was evaluated with statistical criteria (RMSE, R<sup>2</sup> and KGE) and the best model was introduced for each of the parameters. RMSE is the root mean square error, R<sup>2</sup> is the correlation coefficient and KGE is the Kling–Gupta criterion. The results of the support vector machine showed that for the first scenario, the third combination with R<sup>2</sup> = 0.991, RMSE = 0.00565 and KGE = 0.998 for the training mode and R<sup>2</sup> = 0.991, RMSE = 0.00489 and KGE = 0.991 for the testing mode were optimal. For the second scenario, the third combination with R<sup>2</sup> = 0.988, RMSE = 0.0395 and KGE = 0.998 for the training mode and R<sup>2</sup> = 0.988, RMSE = 0.0389 and KGE = 0.993 for the testing mode were selected. Finally, a sensitivity analysis was performed that showed that the y<sub>c</sub>/H and D/H parameters are the most effective parameters for predicting relative energy dissipation and relative pool depth, respectively. |
topic |
relative energy dissipation relative pool depth support vector machine vertical drop horizontal screen |
url |
https://www.mdpi.com/2076-3417/11/9/4238 |
work_keys_str_mv |
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