Author(s): D. P. Solomatine; S. Velickov; J. C. Wust
Linked Author(s):
Keywords: Chaos theory; Neural networks; Water levels; Currents; Prediction; Ship guidance
Abstract: In the ship guidance and navigation the problem of predicting surge water levels and currents is extremely important. There is correlation between data on surge, currents, temperature, air pressure and wind and earlier publications dealt with using the input-output (connectionist) models like neural networks to model these relationships. In the course of this research it appeared also that the surge time series in itself has enough information to make predictions. The experiments with using linear prediction methods including autocorrelation and ARIMA models demonstrated insufficient accuracy. Non-linear methods were used and showed promising results for the short-term prediction. Features of chaotic behaviour were identified in surge, and methods of chaos theory were applied. The predictions are quite accurate. Possible techniques allowing for increase of the prediction accuracy and horizon (wavelet analysis, data mining techniques) were also identified.
Year: 2001