Prediction of sea water levels using wind information and soft computing techniques

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Large variations of sea water levels are a matter of concern for the offshore and coastal locations having shallow water depths. Safety of maritime activities, and properties, as well as human lives at such locations can be ensured by using the accurately predicted water levels. Harmonic analysis is traditionally employed for tide predictions, but often the values of predicted tides and observed (measured) water levels are not identical. The difference between them is called sea level anomaly. This can be attributed to non-inclusion of meteorological parameters as an input for tide prediction. Therefore other prediction techniques become necessary. The earlier studies on sea level predictions indicate better efficiency of alternate techniques such as Artificial Neural Network (ANN) and Genetic Programming (GP), and that most researchers have used sea level time series as model inputs. Present work predicts sea levels indirectly by predicting sea level anomalies (SLAs) using hourly local wind shear velocity components of the present time and up to the previous 12h as inputs at four stations near the USA coastline with the techniques of GP and ANN. The error measures and graphs indicate that predictions are satisfactory.