Artificial neural network for modelling rainfall-runoff
The use of an artificial neural network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Universiti Putra Malaysia Press
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/29458/1/07%20JST-0566-2015.pdf http://psasir.upm.edu.my/id/eprint/29458/ http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2024%20(2)%20Jul.%202016/07%20JST-0566-2015.pdf |
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| Summary: | The use of an artificial neural network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature and therefore, representing their physical characteristics is challenging. In this research, ANN modelling is developed with the use of the MATLAB toolbox for predicting river stream flow coming into the Ringlet reservoir in Cameron Highland, Malaysia. A back propagation algorithm is used to train the ANN. The results indicate that the artificial neural network is a powerful tool in modelling rainfall-runoff. The obtained results could help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought. |
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