Modeling of scour depth and length of a diversion channel flow system with soft computing techniques
This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) techniques, to predict the properties of a scour hole depth (ds) and length (Ls) in a diversion channel flow system. The study considered different geometries of diversion channe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/110300/ http://psasir.upm.edu.my/id/eprint/110300/1/110300.pdf |
| _version_ | 1848865487906144256 |
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| author | Alomari, Nashwan K. Sihag, Parveen Sami Al-Janabi, Ahmed Mohammed Yusuf, Badronnisa |
| author_facet | Alomari, Nashwan K. Sihag, Parveen Sami Al-Janabi, Ahmed Mohammed Yusuf, Badronnisa |
| author_sort | Alomari, Nashwan K. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) techniques, to predict the properties of a scour hole depth (ds) and length (Ls) in a diversion channel flow system. The study considered different geometries of diversion channels (angles and bed widths) and different hydraulic conditions. Four kernel function models for each technique (polynomial kernel function, normalized polynomial kernel function, radial basis kernel, and the Pearson VII function kernel) were evaluated in this investigation. Root mean square error (RMSE) values are 8.3949 for training datasets and 11.6922 for testing datasets, confirming that the normalized polynomial kernel function-based GP outperformed other models in predicting Ls. Regarding predicting ds, the polynomial kernel function-based SVM outperforms other models, recording RMSE of 0.5175 for training datasets and 0.6019 for testing datasets. The sensitivity investigation of input parameters shows that the diversion angle had a major influence in predicting Ls and ds. |
| first_indexed | 2025-11-15T14:05:30Z |
| format | Article |
| id | upm-110300 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:05:30Z |
| publishDate | 2023 |
| publisher | IWA Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1103002024-10-07T03:43:39Z http://psasir.upm.edu.my/id/eprint/110300/ Modeling of scour depth and length of a diversion channel flow system with soft computing techniques Alomari, Nashwan K. Sihag, Parveen Sami Al-Janabi, Ahmed Mohammed Yusuf, Badronnisa This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) techniques, to predict the properties of a scour hole depth (ds) and length (Ls) in a diversion channel flow system. The study considered different geometries of diversion channels (angles and bed widths) and different hydraulic conditions. Four kernel function models for each technique (polynomial kernel function, normalized polynomial kernel function, radial basis kernel, and the Pearson VII function kernel) were evaluated in this investigation. Root mean square error (RMSE) values are 8.3949 for training datasets and 11.6922 for testing datasets, confirming that the normalized polynomial kernel function-based GP outperformed other models in predicting Ls. Regarding predicting ds, the polynomial kernel function-based SVM outperforms other models, recording RMSE of 0.5175 for training datasets and 0.6019 for testing datasets. The sensitivity investigation of input parameters shows that the diversion angle had a major influence in predicting Ls and ds. IWA Publishing 2023 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/110300/1/110300.pdf Alomari, Nashwan K. and Sihag, Parveen and Sami Al-Janabi, Ahmed Mohammed and Yusuf, Badronnisa (2023) Modeling of scour depth and length of a diversion channel flow system with soft computing techniques. Water Supply, 23 (3). pp. 1267-1283. ISSN 1606-9749; eISSN: 1607-0798 https://iwaponline.com/ws/article/23/3/1267/93399/Modeling-of-scour-depth-and-length-of-a-diversion 10.2166/ws.2023.026 |
| spellingShingle | Alomari, Nashwan K. Sihag, Parveen Sami Al-Janabi, Ahmed Mohammed Yusuf, Badronnisa Modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| title | Modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| title_full | Modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| title_fullStr | Modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| title_full_unstemmed | Modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| title_short | Modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| title_sort | modeling of scour depth and length of a diversion channel flow system with soft computing techniques |
| url | http://psasir.upm.edu.my/id/eprint/110300/ http://psasir.upm.edu.my/id/eprint/110300/ http://psasir.upm.edu.my/id/eprint/110300/ http://psasir.upm.edu.my/id/eprint/110300/1/110300.pdf |