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  1. Aida Tayebiyan, Thamer Ahmad Mohammad, Abdul Halim Ghazali, Syamsiah Mashohor
    MyJurnal
    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.
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