ARTIFICIAL NEURAL NETWORK FOR WATER LEVEL PREDICTION IN A RIVER UNDER TIDAL INFLUENCE - Maliana, Sa'ad (Universiti Malaysia Sarawak (UNIMAS), 2004)
This study proposes the application of Artificial Neural Network in the prediction of water level under tidal influence for Sungai Sarawak. 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 Siniawan Station. Distinctive networks were trained and tested using hourly data obtained from the DID Department in Kuching. Various training parameters were considered in order to gain the best prediction possible. The performances of the ANNs were evaluated based on the coefficient of efficiency, E` and the coefficient of correlation, R. The back propagation algorithm was adopted for this study. The optimal model found in this study is the network using two hours of antecedent data, with the combination of learning rate and the number of neurons in the hidden layer of 0.8 and 40. This model generated the highest R Testing of 0.9425 when trained with the scaled conjugate gradient algorithm (trainscg). 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 the limitations of the ANNs had been discussed, based on the results attained in this study.