Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques
Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact sco...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-11-01
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| Series: | Journal of Hydroinformatics |
| Subjects: | |
| Online Access: | http://jhydro.iwaponline.com/content/25/6/2427 |
| _version_ | 1797428905476358144 |
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| author | Veysi Kartal Muhammet Emin Emiroglu Okan Mert Katipoglu Erkan Karakoyun |
| author_facet | Veysi Kartal Muhammet Emin Emiroglu Okan Mert Katipoglu Erkan Karakoyun |
| author_sort | Veysi Kartal |
| collection | DOAJ |
| description | Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).
HIGHLIGHTS
This study analyzed the scour due to water jets using metaheuristic algorithms based on artificial bee colony ABC.;
Metaheuristics optimized feed forward neural network (ABC-FFNN) and pre-processing techniques were used to predict the scour characteristics.; |
| first_indexed | 2024-03-09T09:05:36Z |
| format | Article |
| id | doaj.art-e47680dfa924423f9ca1e322eae9e046 |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-03-09T09:05:36Z |
| publishDate | 2023-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-e47680dfa924423f9ca1e322eae9e0462023-12-02T10:28:04ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342023-11-012562427244310.2166/hydro.2023.230230Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniquesVeysi Kartal0Muhammet Emin Emiroglu1Okan Mert Katipoglu2Erkan Karakoyun3 Engineering Faculty, Department of Civil Engineering, Siirt and Firat University, Elazig, Turkey Engineering Faculty, Department of Civil Engineering, Firat University, Turkey Engineering Faculty, Department of Civil Engineering, Erzincan University, Erzincan, Turkey Engineering and Architecture Faculty, Mus Alparslan University, Mus, Turkey Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863). HIGHLIGHTS This study analyzed the scour due to water jets using metaheuristic algorithms based on artificial bee colony ABC.; Metaheuristics optimized feed forward neural network (ABC-FFNN) and pre-processing techniques were used to predict the scour characteristics.;http://jhydro.iwaponline.com/content/25/6/2427artificial bee colony optimizationartificial neural networkscour hole characteristicssignal processwater jet |
| spellingShingle | Veysi Kartal Muhammet Emin Emiroglu Okan Mert Katipoglu Erkan Karakoyun Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques Journal of Hydroinformatics artificial bee colony optimization artificial neural network scour hole characteristics signal process water jet |
| title | Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques |
| title_full | Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques |
| title_fullStr | Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques |
| title_full_unstemmed | Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques |
| title_short | Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques |
| title_sort | prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony optimized neural network and pre processing techniques |
| topic | artificial bee colony optimization artificial neural network scour hole characteristics signal process water jet |
| url | http://jhydro.iwaponline.com/content/25/6/2427 |
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