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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| Format: | Thesis |
| Language: | English |
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Universiti Malaysia Sarawak, UNIMAS
2004
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| Online Access: | http://ir.unimas.my/id/eprint/3137/ http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf |
| _version_ | 1848835148097781760 |
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| author | Kuok, King Kuok |
| author_facet | Kuok, King Kuok |
| author_sort | Kuok, King Kuok |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| 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). |
| first_indexed | 2025-11-15T06:03:15Z |
| format | Thesis |
| id | unimas-3137 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:03:15Z |
| publishDate | 2004 |
| publisher | Universiti Malaysia Sarawak, UNIMAS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-31372023-06-20T07:50:50Z http://ir.unimas.my/id/eprint/3137/ Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area 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 NonPeerReviewed text en http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf Kuok, King Kuok (2004) Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area. Masters thesis, Universiti Malaysia Sarawak (UNIMAS). |
| spellingShingle | TC Hydraulic engineering. Ocean engineering Kuok, King Kuok Artificial neural networks for rainfall runoff modelling with special reference to Sg. Bedup catchment area |
| title | 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_short | 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 |
| topic | TC Hydraulic engineering. Ocean engineering |
| url | http://ir.unimas.my/id/eprint/3137/ http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf |