Sediment model for natural and man-made channels using general regression neural network
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The Institution of Engineers, Malaysia
2011
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my.unimap-137342011-09-13T06:33:03Z Sediment model for natural and man-made channels using general regression neural network Junaidah, Ariffin Nurashikin, Ahmad Kamal Muhamad Syahreen, Sa’adon Mohd Nasir, Taib Suhaimi, Abdul-Talib Aminuddin, Abd-Ghani Nor Azazi, Zakaria Ahmad Shukri, Yahaya redac01@eng.usm.my redac02@eng.usm.my General regression neural network Man-made channels Natural channels Sediment transport Link to publisher's homepage at http://www.myiem.org.my/ This paper presents a new sediment transport model using general regression neural network (GRNN) that are applicable for both natural and man-made channels. GRNN is a supervised network that trains quickly sparse data sets. The network architecture responses very well with data that is spasmodic in nature than back propagation algorithm. Field data (499 data) extracted from rivers in Selangor, Perak and Kedah are used in the training and testing phases. The model is further tested using hydraulics and sediment data from rivers in the United States namely Sacremento, Atchafalaya, Colorado, Mississippi, Middle Loup, Mountain Creek, Niobrara, Saskatchewan, Oak Creek, Red, Rio Grande rivers and Chop Irrigation Canal. Four independent variables, namely, relative roughness on the bed (R/d50), ratio of shear velocity and fall velocity (U*/Ws), ratio of shear velocity and average velocity (U*/V) and the Froude Number (V/√gy) are used as input variables in the input layer and the total sediment load QT as the output variable. The proposed GRNN sediment model had accurately predicted 89% of the river data sets (local and foreign rivers) with 90% of the predicted values lie in the discrepancy ratio of 0.5 – 2.0. For the sake of illustrations, accuracy of the derived sediment transport model is also measured using smaller range of discrepancy ratios. 2011-09-13T06:33:03Z 2011-09-13T06:33:03Z 2008-09 Article The Journal of the Institution of Engineers, Malaysia, vol. 69(3), 2008, pages 44-58 0126-513X http://www.myiem.org.my/content/iem_journal_2008-179.aspx http://hdl.handle.net/123456789/13734 en The Institution of Engineers, Malaysia |
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| language |
English |
| topic |
General regression neural network Man-made channels Natural channels Sediment transport |
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General regression neural network Man-made channels Natural channels Sediment transport Junaidah, Ariffin Nurashikin, Ahmad Kamal Muhamad Syahreen, Sa’adon Mohd Nasir, Taib Suhaimi, Abdul-Talib Aminuddin, Abd-Ghani Nor Azazi, Zakaria Ahmad Shukri, Yahaya Sediment model for natural and man-made channels using general regression neural network |
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Link to publisher's homepage at http://www.myiem.org.my/ |
| author2 |
redac01@eng.usm.my |
| author_facet |
redac01@eng.usm.my Junaidah, Ariffin Nurashikin, Ahmad Kamal Muhamad Syahreen, Sa’adon Mohd Nasir, Taib Suhaimi, Abdul-Talib Aminuddin, Abd-Ghani Nor Azazi, Zakaria Ahmad Shukri, Yahaya |
| format |
Article |
| author |
Junaidah, Ariffin Nurashikin, Ahmad Kamal Muhamad Syahreen, Sa’adon Mohd Nasir, Taib Suhaimi, Abdul-Talib Aminuddin, Abd-Ghani Nor Azazi, Zakaria Ahmad Shukri, Yahaya |
| author_sort |
Junaidah, Ariffin |
| title |
Sediment model for natural and man-made channels using general regression neural network |
| title_short |
Sediment model for natural and man-made channels using general regression neural network |
| title_full |
Sediment model for natural and man-made channels using general regression neural network |
| title_fullStr |
Sediment model for natural and man-made channels using general regression neural network |
| title_full_unstemmed |
Sediment model for natural and man-made channels using general regression neural network |
| title_sort |
sediment model for natural and man-made channels using general regression neural network |
| publisher |
The Institution of Engineers, Malaysia |
| publishDate |
2011 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/13734 |
| _version_ |
1643790953313468416 |
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13.214268 |