Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods
Abstract The broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and ext...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-08-01
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| Series: | River |
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| Online Access: | https://doi.org/10.1002/rvr2.95 |
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| _version_ | 1846162393972867072 |
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| author | Guiying Shen Dingye Cao Abbas Parsaie |
| author_facet | Guiying Shen Dingye Cao Abbas Parsaie |
| author_sort | Guiying Shen |
| collection | DOAJ |
| description | Abstract The broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and extreme learning machine (ELM) are used to predict the discharge coefficient (Cd) of the triangular broad‐crested weir. The comprehensive analysis shows that the ELM model has high stability, predictive ability, and computational speed. The coefficient of determination (R^2) is 0.99982, the mean absolute percentage error (MAPE) is 0.000261, the Nash‐Sutcliffe coefficient (NSE) is 0.99977, and the root means square error (RMSE) is 4.17E‐05 in the testing phase. The apex angle θ is the most critical parameter affecting the Cd, and the contribution to the Cd is 52.45%. A new computational formula is proposed and compared with the accuracy of empirical formulas, showing that the intelligent method has higher accuracy and efficiency. |
| format | Article |
| id | doaj-art-44dc9f05d8c44dfa89f1b0294878b62d |
| institution | Kabale University |
| issn | 2750-4867 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | River |
| spelling | doaj-art-44dc9f05d8c44dfa89f1b0294878b62d2024-11-20T12:31:16ZengWiley-VCHRiver2750-48672024-08-013331632310.1002/rvr2.95Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methodsGuiying Shen0Dingye Cao1Abbas Parsaie2Department of Hydraulic Engineering, College of Civil Engineering and Architecture Zhejiang University Hangzhou ChinaState Key Laboratory of Eco‐hydraulics in Northwest Arid Region of China Xi'an University of Technology Xi'an ChinaFaculty of Water Sciences Engineering Shahid Chamran University of Ahvaz Ahvaz IranAbstract The broad‐crested weir is convenient to construct and has a small amount of excavation, widely used in practical engineering. Discharge computing has been the focus of research on this structure, thus developing generalized regression neural network (GRNN), genetic programming (GP), and extreme learning machine (ELM) are used to predict the discharge coefficient (Cd) of the triangular broad‐crested weir. The comprehensive analysis shows that the ELM model has high stability, predictive ability, and computational speed. The coefficient of determination (R^2) is 0.99982, the mean absolute percentage error (MAPE) is 0.000261, the Nash‐Sutcliffe coefficient (NSE) is 0.99977, and the root means square error (RMSE) is 4.17E‐05 in the testing phase. The apex angle θ is the most critical parameter affecting the Cd, and the contribution to the Cd is 52.45%. A new computational formula is proposed and compared with the accuracy of empirical formulas, showing that the intelligent method has higher accuracy and efficiency.https://doi.org/10.1002/rvr2.95broad‐crested weirdischarge coefficientmachine learningquantitative analysis |
| spellingShingle | Guiying Shen Dingye Cao Abbas Parsaie Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods River broad‐crested weir discharge coefficient machine learning quantitative analysis |
| title | Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods |
| title_full | Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods |
| title_fullStr | Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods |
| title_full_unstemmed | Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods |
| title_short | Discharge coefficient prediction and sensitivity analysis for triangular broad‐crested weir using machine learning methods |
| title_sort | discharge coefficient prediction and sensitivity analysis for triangular broad crested weir using machine learning methods |
| topic | broad‐crested weir discharge coefficient machine learning quantitative analysis |
| url | https://doi.org/10.1002/rvr2.95 |
| work_keys_str_mv | AT guiyingshen dischargecoefficientpredictionandsensitivityanalysisfortriangularbroadcrestedweirusingmachinelearningmethods AT dingyecao dischargecoefficientpredictionandsensitivityanalysisfortriangularbroadcrestedweirusingmachinelearningmethods AT abbasparsaie dischargecoefficientpredictionandsensitivityanalysisfortriangularbroadcrestedweirusingmachinelearningmethods |