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 U w , the significant wave height H s and the spectral peak period T p . 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.