Prediction of short-term wind and wave conditions for marine operations using a multi-step-ahead decomposition-ANFIS model and quantification of its uncertainty

Short-term predictions of wind and wave properties with a duration of 1–3 days are vital for decision-making during the execution of marine operations. One-step-ahead weather conditions can be accurately predicted via various methods. However, prediction over long horizons is challenging since multi-step-ahead prediction is typically faced with growing uncertainties. In this study, a hybrid method for predicting multi-step-ahead wind and wave conditions is proposed, which combines a decomposition technique and the adaptive-network-based fuzzy inference system (ANFIS). First, the decomposition technique is applied to obtain stationary time series. Then, multi-step-ahead forecasting is conducted using ANFIS, in which three multi-step-ahead models (the M-1, M-N and M-1 slope models) are employed. To quantify the forecast uncertainty, the mean value and standard deviation of the error factor are calculated. The proposed method is evaluated by multi-step-ahead predictions within 24 h of wind and wave conditions at the North Sea center utilizing hourly time series of the mean wind speed Uw, the significant wave height Hs and the spectral peak period Tp. The results demonstrate that the forecast uncertainty increases with the prediction horizon, and a prediction range determined by the error factor provides a basic reference for the use of predicted environmental conditions for marine operations.