Prediction of Backwater Level of Bridge Constriction using ANN
Bridge constriction in channels usually causes afflux which results in increase in backwater level well above the normal level and may possibly result in overflow on the flood plain surrounding the channel during flooding period. This paper uses Artificial Neural Network to predict the afflux based...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2012
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/8578 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513438899765248 |
|---|---|
| author | Atabay, Serter |
| author2 | Abdalla, Jamal Erduran, Kutsi Mortula, Maruf Seckin, Galip |
| author2_role | author author author author |
| author_facet | Atabay, Serter Abdalla, Jamal Erduran, Kutsi Mortula, Maruf Seckin, Galip |
| author_role | author |
| dc.creator.none.fl_str_mv | Atabay, Serter Abdalla, Jamal Erduran, Kutsi Mortula, Maruf Seckin, Galip |
| dc.date.none.fl_str_mv | 2012-12 2016-10-26T08:32:17Z 2016-10-26T08:32:17Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Atabay, Serter, Jamal A. Abdalla, Kutsi Erduran, Maruf Mortula, and Galip Seckin. "Prediction of Backwater Level of Bridge Constriction using ANN." Water Management 165, no. WM1 (2012) 9781457700057 http://hdl.handle.net/11073/8578 10.1109/ICMSAO.2011.5775538 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Water Management https://dx.doi.org/10.1109/ICMSAO.2011.5775538 |
| dc.subject.none.fl_str_mv | Bridge Constriction Neural Network Afflux |
| dc.title.none.fl_str_mv | Prediction of Backwater Level of Bridge Constriction using ANN |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Bridge constriction in channels usually causes afflux which results in increase in backwater level well above the normal level and may possibly result in overflow on the flood plain surrounding the channel during flooding period. This paper uses Artificial Neural Network to predict the afflux based on the parameters including coefficient of frictions of main channel (nmc) and of floodplain (nfp), bridge width (b) and flow discharge (Q). A Multi-Layer Perceptron (MLP) ANN is used to predict the afflux using these parameters. The training and testing data are the result of experimental investigation. It is observed that the afflux values predicted by the ANN model are very accurate compared to the experimentally measured values with a Normalized Mean Square Error (NMSE) of 0.002 and a Correlation Coefficient of 0.999. The developed ANN model can be used safely to conduct a parametric study to investigate the influence of the parameters nmc, nfp, b and Q on the afflux of a bridge constriction with piers. |
| format | article |
| id | aus_82fa9d21ca3db61f3a8f14c40b7ad7e1 |
| identifier_str_mv | Atabay, Serter, Jamal A. Abdalla, Kutsi Erduran, Maruf Mortula, and Galip Seckin. "Prediction of Backwater Level of Bridge Constriction using ANN." Water Management 165, no. WM1 (2012) 9781457700057 10.1109/ICMSAO.2011.5775538 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/8578 |
| publishDate | 2012 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Prediction of Backwater Level of Bridge Constriction using ANNAtabay, SerterAbdalla, JamalErduran, KutsiMortula, MarufSeckin, GalipBridge ConstrictionNeural NetworkAffluxBridge constriction in channels usually causes afflux which results in increase in backwater level well above the normal level and may possibly result in overflow on the flood plain surrounding the channel during flooding period. This paper uses Artificial Neural Network to predict the afflux based on the parameters including coefficient of frictions of main channel (nmc) and of floodplain (nfp), bridge width (b) and flow discharge (Q). A Multi-Layer Perceptron (MLP) ANN is used to predict the afflux using these parameters. The training and testing data are the result of experimental investigation. It is observed that the afflux values predicted by the ANN model are very accurate compared to the experimentally measured values with a Normalized Mean Square Error (NMSE) of 0.002 and a Correlation Coefficient of 0.999. The developed ANN model can be used safely to conduct a parametric study to investigate the influence of the parameters nmc, nfp, b and Q on the afflux of a bridge constriction with piers.2016-10-26T08:32:17Z2016-10-26T08:32:17Z2012-12info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAtabay, Serter, Jamal A. Abdalla, Kutsi Erduran, Maruf Mortula, and Galip Seckin. "Prediction of Backwater Level of Bridge Constriction using ANN." Water Management 165, no. WM1 (2012)9781457700057http://hdl.handle.net/11073/857810.1109/ICMSAO.2011.5775538en_USWater Managementhttps://dx.doi.org/10.1109/ICMSAO.2011.5775538oai:repository.aus.edu:11073/85782024-08-22T12:15:03Z |
| spellingShingle | Prediction of Backwater Level of Bridge Constriction using ANN Atabay, Serter Bridge Constriction Neural Network Afflux |
| status_str | publishedVersion |
| title | Prediction of Backwater Level of Bridge Constriction using ANN |
| title_full | Prediction of Backwater Level of Bridge Constriction using ANN |
| title_fullStr | Prediction of Backwater Level of Bridge Constriction using ANN |
| title_full_unstemmed | Prediction of Backwater Level of Bridge Constriction using ANN |
| title_short | Prediction of Backwater Level of Bridge Constriction using ANN |
| title_sort | Prediction of Backwater Level of Bridge Constriction using ANN |
| topic | Bridge Constriction Neural Network Afflux |
| url | http://hdl.handle.net/11073/8578 |