Abstract
Accurate tide prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction was carried out using the harmonic method, which is based on the identification of the harmonic tidal constituents existing in the tidal record. Unfortunately, however, unless long tidal records are available at the tide gauges, some important tidal constituents may not be identified. This, in turn, deteriorates the accuracy of the tidal prediction. More recently, a sequential least-squares prediction method capable of using relatively short tidal records was developed. This method allows for the modifications and corrections of the original solution of the tidal constituents when new observations and parameters are included. Although it reduces the computation time significantly compared to the batch harmonic method, this method exhibits large residuals particularly when very short tidal records are used. To overcome the limitations of the sequential least-squares method, a neural network-based model is developed for sequentially predicting the tidal heights using tide data series collected at various tide gauges. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Tide data from three tide gauges are used to validate the model. A comparison is made between the developed neural network model and the sequential least squares method for tidal prediction. It is shown that the accuracy level of the tidal prediction has improved by a factor of 5 when using the neural network model.