Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area
Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natura...
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| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2004
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| الوصول للمادة أونلاين: | http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf |
| الوسوم: |
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my-unimas-ir.31372023-06-20T07:50:50Z Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area 2004 Kuok, King Kuok TC Hydraulic engineering. Ocean engineering Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natural behavior of hydrological processes is appropriate for the application ANN in hydrology. A rainfall runoff model for Sungai Bedup Basin in Sarawak was built using three different ANN architectures namely Multilayer perceptron (MLP), Recurrent (REC) and Radial Basic function (RBF). Universiti Malaysia Sarawak, UNIMAS 2004 Thesis http://ir.unimas.my/id/eprint/3137/ http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Engineering |
| institution |
Universiti Malaysia Sarawak |
| collection |
UNIMAS Institutional Repository |
| language |
English |
| topic |
TC Hydraulic engineering Ocean engineering |
| spellingShingle |
TC Hydraulic engineering Ocean engineering Kuok, King Kuok Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| description |
Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natural behavior of hydrological processes is appropriate for the application ANN in hydrology. A rainfall runoff model for Sungai Bedup Basin in Sarawak was built using three different ANN architectures namely Multilayer perceptron (MLP), Recurrent (REC) and Radial Basic function (RBF). |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Kuok, King Kuok |
| author_facet |
Kuok, King Kuok |
| author_sort |
Kuok, King Kuok |
| title |
Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| title_short |
Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| title_full |
Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| title_fullStr |
Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| title_full_unstemmed |
Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| title_sort |
artificial neural networks for rainfall runoff modelling with special reference to sg. bedup catchment area |
| granting_institution |
Universiti Malaysia Sarawak (UNIMAS) |
| granting_department |
Faculty of Engineering |
| publishDate |
2004 |
| url |
http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf |
| _version_ |
1783727911482687488 |