Regression models for predicting the effect of trash rack on flow properties at power intakes
Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models – Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB) – and a proposed hybrid m...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2359022 |
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| author | Shuguang Li Sultan Noman Qasem Hojat Karami Ely Salwana Alireza Rezaei Danyal Shahmirzadi Shahab S. Band |
| author_facet | Shuguang Li Sultan Noman Qasem Hojat Karami Ely Salwana Alireza Rezaei Danyal Shahmirzadi Shahab S. Band |
| author_sort | Shuguang Li |
| collection | DOAJ |
| description | Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models – Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB) – and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3 m3 with various submergence depths and Froude numbers on nine trash racks with 63.7%–84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ([Formula: see text]) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for [Formula: see text] estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), [Formula: see text] (0.9971), WI (0.9727) for [Formula: see text] estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation. |
| format | Article |
| id | doaj-art-7ced0a45cccb4d4ead602bf39eb52ccd |
| institution | Kabale University |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-7ced0a45cccb4d4ead602bf39eb52ccd2024-12-09T09:43:45ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2359022Regression models for predicting the effect of trash rack on flow properties at power intakesShuguang Li0Sultan Noman Qasem1Hojat Karami2Ely Salwana3Alireza Rezaei4Danyal Shahmirzadi5Shahab S. Band6School of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaComputer Science Department, Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi ArabiaCivil Engineering Department, Semnan University, Semnan, IranInstitute of Visual Informatics, Universiti Kebangsaan Malaysia, Selangor, MalaysiaCivil Engineering Department, Semnan University, Semnan, IranGraduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliou, TaiwanInternational Graduate School of AI, National Yunlin University of Science and Technology, Yunlin, TaiwanVortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models – Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB) – and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3 m3 with various submergence depths and Froude numbers on nine trash racks with 63.7%–84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ([Formula: see text]) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for [Formula: see text] estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), [Formula: see text] (0.9971), WI (0.9727) for [Formula: see text] estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation.https://www.tandfonline.com/doi/10.1080/19942060.2024.2359022Hydro power plantexperimental modellingvortex strengthregression models |
| spellingShingle | Shuguang Li Sultan Noman Qasem Hojat Karami Ely Salwana Alireza Rezaei Danyal Shahmirzadi Shahab S. Band Regression models for predicting the effect of trash rack on flow properties at power intakes Engineering Applications of Computational Fluid Mechanics Hydro power plant experimental modelling vortex strength regression models |
| title | Regression models for predicting the effect of trash rack on flow properties at power intakes |
| title_full | Regression models for predicting the effect of trash rack on flow properties at power intakes |
| title_fullStr | Regression models for predicting the effect of trash rack on flow properties at power intakes |
| title_full_unstemmed | Regression models for predicting the effect of trash rack on flow properties at power intakes |
| title_short | Regression models for predicting the effect of trash rack on flow properties at power intakes |
| title_sort | regression models for predicting the effect of trash rack on flow properties at power intakes |
| topic | Hydro power plant experimental modelling vortex strength regression models |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2359022 |
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