Volume 26, Issue 4 (Winiter 2023)                   jwss 2023, 26(4): 91-104 | Back to browse issues page


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Shahinejad B, Parsaei A, Yonesi H, Shamsi Z, Arshia A. Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models. jwss 2023; 26 (4) :91-104
URL: http://jstnar.iut.ac.ir/article-1-4246-en.html
Lorestan University , shahinejad.b@lu.ac.ir
Abstract:   (1204 Views)
In the present study, the flow rate in flues containing lateral semi-cylinders (SMBF) was simulated and estimated under free and submerged conditions using back vector machine models (SVM), spin multivariate adaptive regression (MARS), and multilayer artificial neural network (MLPNN) model. In free flow mode, the dimensionless parameters extracted from the dimensional analysis include the ratio of upstream flow to throat width and contraction ratio (throat width to channel width), and in the submerged state, in addition to these two parameters, the depth-to-throat width, and bottom-depth parameters upstream depth were used as input and the two-dimensional form of flow rate was used as the output of the models. The results showed that in free flow mode in the validation stage, the MARS model with statistical indices of R2 = 0.985, RMSE = 0.008, MAPE = 0.87%, and the SVM model with statistical indices of  R2 = 0.971, RMSE = 0.0012, MAPE =1.376%, and MLPNN model with statistical indices of R2 = 0.973,  RMSE = 0.011, MAPE = 1.304% have modeled and predicted the flow rate. In the submerged state, the statistical indices of the developed MARS model were R2 = 0.978, RMSE = 0.018, MAPE = 3.6%, and the statistical indices of the SVM model were R2 = 0.988, RMSE = 0.014, 2%. MAPE = 4, and the statistical indicators of the MLPNN model were R2 = 0.966, RMSE = 0.022, and MAPE = 5.7%. In the development of SVM and MLPNN models, radial kernel and hyperbolic tangent functions were used, respectively.
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Type of Study: Research | Subject: Ggeneral
Received: 2022/02/7 | Accepted: 2022/05/14 | Published: 2023/03/1

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