Tidal Level Forecasting Using ANN

Abstract

Appropriate Tidal level data is indispensable requirement for planning and safe execution of harbour projects, other coastal structures and also for optimal movement of ships. Tidal currents are also used in deciding the orientation of berthing structures and in identifying the area, dominated by eddy currents. Conventional tidal forecasting using least squares method for the prediction of long-term tidal levels requires large number of parameters for harmonic analysis. Accuracy of results depends on the length of measured samples. This paper presents an Artificial Neural Network (ANN) model for forecasting the tidal-levels using the limited measured data as an alternative to conventional harmonic analysis. ANN model developed for forecasting the tidal data is trained with different learning algorithms, number of neurons in its hidden layer and number of epochs (iterations). The aim was to develop a network, which provides a simulated tide which would be in well agreement with the actual tide. Efforts were made to forecast the time series of tidal levels based on the previous data.Study shows that the Feed-Forward Back Propagation (FFBP) network with Levenberg-Marquardt (LM) algorithm provides good correlations as compared to other algorithms. Results were compared using statistical techniques such as correlation coefficients, RMSE and Nash-Sutcliff efficiency. ANN is found to be efficient technique in forecasting the tidal data time series for a short duration. This method can be used for short term predictions of tidal time series, however model developed would remain site specific. This model was also used for predicting the tide at a far distance tidal station using the tide at the local station. It was observed that prediction of tidal levels at the far distant station is not accurately done by the neural network and shall not be used for such purposes.