Hourly water level prediction of sungai bedup catchement using pre-developed ANNs model from Siniawan catchment - Hong,, Calvin Chiao Chun (Universiti Malaysia Sarawak (UNIMAS), 2009)
This study proposes the application of Artificial Neural Network in the prediction of water level under tidal influence for Sadong Basin. An Artificial Neural Network is undoubtedly a robust tool for forecasting various non-linear hydrologic processes, including the water level prediction. It is a flexible mathematical structure which is capable to generalize patterns in imprecise or noisy and ambiguous input and output data sets. In this study, the ANNs were developed specifically to forecast the hourly water level for Sg. Bedup Station. Distinctive networks were trained and tested using hourly data obtained from the DID Department in Kuching. The performances of the ANNs were evaluated based on the coefficient of efficiency, E2 and the coefficient of correlation, R. The back propagation algorithm was adopted for this study. The models used in this study is the network trained with scaled conjugate gradient algorithm (trainscg) with two hours of antecedent data, learning rate and the number of neurons in the hidden layer of 0.8 and 40 respectively. In this study, the models generated the R value for testing of 1.00 when it trained. It has been found that the ANN has the potential to solve the problems of water level prediction. After appropriate trainings, they are able to generate satisfactory results during both of the training and testing phases. Further, the strength and limitations of ANNs had been discussed, based on the resulted obtained in this study.