Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | fas |
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Isfahan University of Technology
2023-03-01
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| Series: | علوم آب و خاک |
| Subjects: | |
| Online Access: | http://jstnar.iut.ac.ir/article-1-4246-en.html |
| _version_ | 1797845563249524736 |
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| author | B. Shahinejad A. Parsaei H. Yonesi Z. Shamsi A. Arshia |
| author_facet | B. Shahinejad A. Parsaei H. Yonesi Z. Shamsi A. Arshia |
| author_sort | B. Shahinejad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T17:41:03Z |
| format | Article |
| id | doaj.art-c5c8a0b7c8724084b9b3da74feac074d |
| institution | Directory Open Access Journal |
| issn | 2476-3594 2476-5554 |
| language | fas |
| last_indexed | 2024-04-09T17:41:03Z |
| publishDate | 2023-03-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | علوم آب و خاک |
| spelling | doaj.art-c5c8a0b7c8724084b9b3da74feac074d2023-04-17T05:42:14ZfasIsfahan University of Technologyعلوم آب و خاک2476-35942476-55542023-03-0126491104Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation ModelsB. Shahinejad0A. Parsaei1H. Yonesi2Z. Shamsi3A. Arshia4 Lorestan University Shahid Chamran University of Ahvaz Lorestan University Lorestan University Lorestan University 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.http://jstnar.iut.ac.ir/article-1-4246-en.htmlartificial neural networksmbf flumeswater transfer channelsupport vector machineside half cylinders |
| spellingShingle | B. Shahinejad A. Parsaei H. Yonesi Z. Shamsi A. Arshia Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models علوم آب و خاک artificial neural network smbf flumes water transfer channel support vector machine side half cylinders |
| title | Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models |
| title_full | Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models |
| title_fullStr | Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models |
| title_full_unstemmed | Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models |
| title_short | Modeling and Estimating Flow Rate in SMBF Flumes using Soft Computation Models |
| title_sort | modeling and estimating flow rate in smbf flumes using soft computation models |
| topic | artificial neural network smbf flumes water transfer channel support vector machine side half cylinders |
| url | http://jstnar.iut.ac.ir/article-1-4246-en.html |
| work_keys_str_mv | AT bshahinejad modelingandestimatingflowrateinsmbfflumesusingsoftcomputationmodels AT aparsaei modelingandestimatingflowrateinsmbfflumesusingsoftcomputationmodels AT hyonesi modelingandestimatingflowrateinsmbfflumesusingsoftcomputationmodels AT zshamsi modelingandestimatingflowrateinsmbfflumesusingsoftcomputationmodels AT aarshia modelingandestimatingflowrateinsmbfflumesusingsoftcomputationmodels |