Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models
Flip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stabili...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123023007314 |
| _version_ | 1797385177188532224 |
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| author | Mehdi Fuladipanah H Md Azamathulla Kiran Tota-Maharaj Vishwanadham Mandala Aaron Chadee |
| author_facet | Mehdi Fuladipanah H Md Azamathulla Kiran Tota-Maharaj Vishwanadham Mandala Aaron Chadee |
| author_sort | Mehdi Fuladipanah |
| collection | DOAJ |
| description | Flip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stability. The accurate prediction of scour hole depth is a crucial area of the present research work. This study endeavors to employ four data-driven models (DDMs), namely Support Vector Machine (SVM), Gene Expression Programming (GEP), Multilayer Perceptron (MLP), and Multivariate Adaptive Regression Splines (MARS), in combination with five selected empirical equations. The objective is to accurately predict scour depth utilizing field-collected data from site number 84. Relative scour depth, dsH1, was simulated based on the readily extracted parameter i.e. Froude number, Fr=qgH13. The evaluation of model performance was conducted using fundamental metrics, including root mean square error (RMSE), coefficient of determination (R2), mean average error (MAE), and the maximum value of the developed discrepancy ratio (DDRmax). Among the DDMs, the MARS model demonstrated superior performance in both the training and testing phases. In the training phase, it yielded metrics (RMSE = 0.08665, MAE = 0.05714, R2 = 0.99169, DDRmax = 4.519), and in the testing phase, it produced metrics (RMSE = 0.0252, MAE = 0.0170, R2 = 0.09933, DDRmax = 9.144). This exceptional performance of the MARS model surpassed the initially selected (Wu, 1973) [1] experimental model, which exhibited metrics (RMSE = 0.39667, MAE = 0.17463, R2 = 0.96172, DDR = 1.428). The evaluation indices conclusively establish the MARS method's absolute superiority over the experimental approach proposed by Wu (1973) [1]. |
| first_indexed | 2024-03-08T21:50:23Z |
| format | Article |
| id | doaj.art-d2f431e2017a46f9936af8cc8f347644 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-03-08T21:50:23Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-d2f431e2017a46f9936af8cc8f3476442023-12-20T07:36:22ZengElsevierResults in Engineering2590-12302023-12-0120101604Precise forecasting of scour depth downstream of flip bucket spillway through data-driven modelsMehdi Fuladipanah0H Md Azamathulla1Kiran Tota-Maharaj2Vishwanadham Mandala3Aaron Chadee4Department of Civil Engineering, Ramhormoz Branch, Islamic Azad University, Ramhormoz, Iran; Corresponding author.Civil Engineering and Environmental Engineering, The University of the West Indies, St. Augustine Campus, TrinidadDepartment of Civil Engineering, School of Infrastructure & Sustainable Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham, Aston Triangle, Birmingham, UKEnterprise Architect, Indiana University, Bloomington, IN, US.MS in Data Science, IU Bloomington, USACivil and Environmental Engineering, University of the West Indies, St Augustine Campus, TrinidadFlip-bucket spillways are utilized in hydraulic engineering to diminish the kinetic energy of flowing water by redirecting the flow jet into the air. In the downstream stailing basin with low tail-water, sediment particles movement results in scour hole formation, posing a threat to spillway stability. The accurate prediction of scour hole depth is a crucial area of the present research work. This study endeavors to employ four data-driven models (DDMs), namely Support Vector Machine (SVM), Gene Expression Programming (GEP), Multilayer Perceptron (MLP), and Multivariate Adaptive Regression Splines (MARS), in combination with five selected empirical equations. The objective is to accurately predict scour depth utilizing field-collected data from site number 84. Relative scour depth, dsH1, was simulated based on the readily extracted parameter i.e. Froude number, Fr=qgH13. The evaluation of model performance was conducted using fundamental metrics, including root mean square error (RMSE), coefficient of determination (R2), mean average error (MAE), and the maximum value of the developed discrepancy ratio (DDRmax). Among the DDMs, the MARS model demonstrated superior performance in both the training and testing phases. In the training phase, it yielded metrics (RMSE = 0.08665, MAE = 0.05714, R2 = 0.99169, DDRmax = 4.519), and in the testing phase, it produced metrics (RMSE = 0.0252, MAE = 0.0170, R2 = 0.09933, DDRmax = 9.144). This exceptional performance of the MARS model surpassed the initially selected (Wu, 1973) [1] experimental model, which exhibited metrics (RMSE = 0.39667, MAE = 0.17463, R2 = 0.96172, DDR = 1.428). The evaluation indices conclusively establish the MARS method's absolute superiority over the experimental approach proposed by Wu (1973) [1].http://www.sciencedirect.com/science/article/pii/S2590123023007314Dam safetyMachine learningPerformance assessmentScour depth forecasting |
| spellingShingle | Mehdi Fuladipanah H Md Azamathulla Kiran Tota-Maharaj Vishwanadham Mandala Aaron Chadee Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models Results in Engineering Dam safety Machine learning Performance assessment Scour depth forecasting |
| title | Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models |
| title_full | Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models |
| title_fullStr | Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models |
| title_full_unstemmed | Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models |
| title_short | Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models |
| title_sort | precise forecasting of scour depth downstream of flip bucket spillway through data driven models |
| topic | Dam safety Machine learning Performance assessment Scour depth forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2590123023007314 |
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