Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area

Abstract

The prediction of tidal currents in the coastal region on a real-time or online basis is useful in taking operation- and planning-related decisions such as towing of vessels and monitoring of oil slick movements. Currently, however, this is done in offline mode on the basis of the statistical method of harmonic analysis involving fitting of harmonic functions to measured data. Alternatively, numerical solutions of hydrodynamic models can also provide spatial and temporal information on currents. Owing to the complex real sea conditions, such methods may not always yield satisfactory results. This paper discusses a few alternative approaches based on the soft computing tools of artificial neural networks (ANNs) and genetic programming (GP), as well as the hard mathematical approaches of stochastic and statistical methods. The suggested schemes use only a univariate time series of currents to forecast their future values. The measurements of coastal currents made at two locations in the Gulf of Khambhat along the west coast of India have been analysed. The current predictions over a time step of 20 min, a few hours, and a day at the specified locations were carried out. It was found that the soft computing schemes of GP and ANN performed better than the traditional hard technique of harmonic analysis in the present application. This work should initiate more application of GP in coastal engineering. Addressing the problem of current predictions in real-time mode based on analysis of observed time series of ocean currents is a specialty of this work.