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...

Full description

Bibliographic Details
Main Authors: Rasoul Daneshfaraz, Ehsan Aminvash, Amir Ghaderi, John Abraham, Mohammad Bagherzadeh
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/9/4238
id doaj-d7addc8c7d8d44d7a5d25628d21c4c78
record_format Article
spelling 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 AT rasouldaneshfaraz svmperformanceforpredictingtheeffectofhorizontalscreendiametersonthehydraulicparametersofaverticaldrop
AT ehsanaminvash svmperformanceforpredictingtheeffectofhorizontalscreendiametersonthehydraulicparametersofaverticaldrop
AT amirghaderi svmperformanceforpredictingtheeffectofhorizontalscreendiametersonthehydraulicparametersofaverticaldrop
AT johnabraham svmperformanceforpredictingtheeffectofhorizontalscreendiametersonthehydraulicparametersofaverticaldrop
AT mohammadbagherzadeh svmperformanceforpredictingtheeffectofhorizontalscreendiametersonthehydraulicparametersofaverticaldrop
_version_ 1721417663986532352