A fuzzy inference system for wind-wave modeling

Forecasting of sea-state characteristics, with warning time of a few hours, appears a necessity in Operational Oceanography, linking sophisticated marine monitoring systems with forecasting modeling tools. In this paper, instead of using conventional numerical models, a Takagi–Sugeno-rule-based Fuzzy Inference System (FIS) was developed aiming at forecasting wave parameters based on the wind speed and direction, and the lagged-wave characteristics. Initial and final antecedent fuzzy membership functions were identified using the subtractive clustering method. The model was applied on the wind and wave dataset recorded in years 2000–2006 by an oceanographic buoy deployed in the Aegean Sea. The model showed perfect fit for the training period (2000–2005; 12,274 data points), and expanded its hindcasting ability during 2006 (1044 data points), as the verification part of the series. Model results, for a lead time of 3 h, showed good agreement between the predicted and the observed significant wave height (RMSE=0.216) and zero-up-crossing period (RMSE=0.315). According to other model performance criteria, the fuzzy model slightly underpredicted both wave characteristics (the linear regression slope was 0.911 for wave height and 0.788 for wave period), and reduced its forecasting ability at higher prediction intervals (+6 to +12 h). Overall, model results illustrated that the developed FIS could serve as a valuable tool for the operational prediction of wave characteristics in Northern Aegean Sea, through the utilization of the POSEIDON network.