Author/Authors :
Tayebiyan, Aida Department of Civil Engineering - Faculty of Engineering - Universiti Putra Malaysia, Selangor, Malaysia , Ahmad Mohammad, Thamer Department of Civil Engineering - Faculty of Engineering - Universiti Putra Malaysia, Selangor, Malaysia , Ghazali, Abdul Halim Department of Civil Engineering - Faculty of Engineering - Universiti Putra Malaysia, Selangor, Malaysia , Mashohor, Syamsiah Department of Computer and Communication Systems Engineering - Faculty of Engineering - Universiti Putra Malaysia, Selangor, Malaysia
Abstract :
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 rainfallrunoff.
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.
