SVM Performance for Predicting the Effect of Horizontal Screen Diameters on the Hydraulic Parameters of a Vertical Drop

Applied Sciences

Rasoul Daneshfaraz¹  Ehsan Aminvash¹  Amir Ghaderi²  Mohammad Bagherzadeh³  John Abraham⁴ 

[1] Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh 8311155181, Iran;Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan 537138791, Iran;Department of Civil Engineering, Faculty of engineering, Urmia University, Urmia 5756151818, Iran;School of Engineering, University of St. Thomas, St. Paul, MN 55105, USA;

关键词: relative energy dissipation;    relative pool depth;    support vector machine;    vertical drop;    horizontal screen;   

DOI  :  10.3390/app11094238

来源: DOAJ

【 摘 要 】

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, R² and KGE) and the best model was introduced for each of the parameters. RMSE is the root mean square error, R² 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 R² = 0.991, RMSE = 0.00565 and KGE = 0.998 for the training mode and R² = 0.991, RMSE = 0.00489 and KGE = 0.991 for the testing mode were optimal. For the second scenario, the third combination with R² = 0.988, RMSE = 0.0395 and KGE = 0.998 for the training mode and R² = 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_c/H and D/H parameters are the most effective parameters for predicting relative energy dissipation and relative pool depth, respectively.

【 授权许可】

Unknown