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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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/3137/1/Kuok.pdf |
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| Summary: | 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). |
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