Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT
Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations (CP′) is critical for hydraulic engineers. This study proposed pred...
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| Format: | Article |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2495631 |
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| author | Tzu-Chia Chen Biju Theruvil Sayed Maria Jade Catalan Opulencia Raed H. C. Alfilh Maki Mahdi Abdulhasan Sayed Hashmat Sadat |
| author_facet | Tzu-Chia Chen Biju Theruvil Sayed Maria Jade Catalan Opulencia Raed H. C. Alfilh Maki Mahdi Abdulhasan Sayed Hashmat Sadat |
| author_sort | Tzu-Chia Chen |
| collection | DOAJ |
| description | Pressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations (CP′) is critical for hydraulic engineers. This study proposed predictive soft computing models to estimate CP′ on sloping channels. Therefore, three robust soft computing methods, including extreme learning machine (ELM), group method data of handling (GMDH), and M5 model tree (M5MT), were used to estimate CP′. The results revealed that ELM was more accurate than GMDH and M5MT methods when comparing statistical indices, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), index agreement (Ia), and BIAS values. The performance of ELM was found to be more accurate (CC = 0.9183, RMSE = 0.0067, MAE = 0.0051, SI = 11.88%, Ia = 0.9569) when compared with the results of GMDH (CC = 0.8818, RMSE = 0.0078, MAE = 0.0058, SI = 13.89%, Ia = 0.9361) and M5MT (CC = 0.6883, RMSE = 0.0120, MAE = 0.0090, SI = 21.28%, Ia = 0.7905) in the testing stage. In addition, the BIAS values revealed that ELM slightly overestimated the values of CP′, especially at the peak point compared with GMDH and M5MT results. Overall, the suggested soft computing techniques worked well for predicting pressure fluctuation changes in the hydraulic jump. |
| format | Article |
| id | doaj-art-79f0aa16249d4198adfe3815efdd3909 |
| institution | OA Journals |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-79f0aa16249d4198adfe3815efdd39092025-08-20T02:07:19ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/2495631Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MTTzu-Chia Chen0Biju Theruvil Sayed1Maria Jade Catalan Opulencia2Raed H. C. Alfilh3Maki Mahdi Abdulhasan4Sayed Hashmat Sadat5Department of Industrial Engineering and ManagementDepartment of Computer Science Dhofar UniversityCollege of Business Administration Ajman UniversityRefrigeration and Air-conditioning Technical Engineering DepartmentAl-Nisour University CollegeKabul UniversityPressure fluctuations are a critical phenomenon that can endanger the safety and stability of hydraulic structures, especially stilling basins. Hence, the accurate estimation of the dimensionless coefficient of pressure fluctuations (CP′) is critical for hydraulic engineers. This study proposed predictive soft computing models to estimate CP′ on sloping channels. Therefore, three robust soft computing methods, including extreme learning machine (ELM), group method data of handling (GMDH), and M5 model tree (M5MT), were used to estimate CP′. The results revealed that ELM was more accurate than GMDH and M5MT methods when comparing statistical indices, including correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), index agreement (Ia), and BIAS values. The performance of ELM was found to be more accurate (CC = 0.9183, RMSE = 0.0067, MAE = 0.0051, SI = 11.88%, Ia = 0.9569) when compared with the results of GMDH (CC = 0.8818, RMSE = 0.0078, MAE = 0.0058, SI = 13.89%, Ia = 0.9361) and M5MT (CC = 0.6883, RMSE = 0.0120, MAE = 0.0090, SI = 21.28%, Ia = 0.7905) in the testing stage. In addition, the BIAS values revealed that ELM slightly overestimated the values of CP′, especially at the peak point compared with GMDH and M5MT results. Overall, the suggested soft computing techniques worked well for predicting pressure fluctuation changes in the hydraulic jump.http://dx.doi.org/10.1155/2022/2495631 |
| spellingShingle | Tzu-Chia Chen Biju Theruvil Sayed Maria Jade Catalan Opulencia Raed H. C. Alfilh Maki Mahdi Abdulhasan Sayed Hashmat Sadat Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT Advances in Civil Engineering |
| title | Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT |
| title_full | Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT |
| title_fullStr | Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT |
| title_full_unstemmed | Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT |
| title_short | Prediction of the Coefficient of Pressure Fluctuations during the Hydraulic Jump Using ELM, GMDH, and M5MT |
| title_sort | prediction of the coefficient of pressure fluctuations during the hydraulic jump using elm gmdh and m5mt |
| url | http://dx.doi.org/10.1155/2022/2495631 |
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