Wave prediction using neural networks at New Mangalore Port along west coast of India

Estimation of wave parameters is of great importance in coastal activities such as design studies for harbour, inshore and offshore structures, coastal erosion, sediment transport and wave energy estimation. Traditional methods like semi empirical formulations and numerical models have disadvantages of excessive data requirement, time consumption and are tedious to carry out. Artificial Intelligent methods like ANN (Artificial Neural Network) are powerful and flexible modeling tool is being widely used in coastal engineering field since last two decades for solving various problems related to time series forecasting of waves and tides; prediction of sea-bed liquefaction and scour depth; and estimation of design parameters of coastal engineering structures. It imbibes the qualities of exploiting non-linearity, adaptability to adjust free parameters by mapping input output data sets using various learning algorithms and fault tolerance. Using input and output information, intelligent systems enabled to estimate the hidden law behind the information and to find their relations among them. In the present study attempt has been made to predict waves at New Mangalore Port Trust (NMPT) located along the west coast of India using Feed Forward Back Propagation (FFBP) with LM algorithm and a recurrent network called Non-linear Auto Regressive with exogenous input(NARX) network. Field data of NMPT has been used to train and test the network performance, which are measured in terms of mean square error (mse) and correlation coefficient (r). Effect of network architecture on the performance of model has been studied. Correlation coefficient is found to be 0.94 in case of NARX predictions indicating better performance than FFBP network whose 'r' value is 0.9. It was found that for time series prediction NARX network outperform FFBP network not only in terms of accuracy but also in terms of time required for computation. (C) 2015 The Authors. Published by Elsevier B.V.