Short-term forecasting of surface wave elevation based on an autoregressive model

Predicting ocean wave elevations on a wave-by-wave basis has been gaining increased attention recently. It has the potential to improve the efficiency and safety of a wide range of offshore operations, such as crane lifts and control of wave energy converters. This study investigates the use of a data-driven technique, specifically the autoregressive model, to predict surface wave elevation in the ocean based only on past measurements at a specific location. The confidence interval of the prediction is provided to quantify uncertainty. The influence of bandwidth on prediction and the cut-off frequency, which is a compromise between improvement in the prediction accuracy and the quantity of discarded wave components is also explored. In this study, the data are digitally filtered into low- and high-pass components. The prediction demonstrates significant improvement in accuracy and prediction horizon compared to the original unfiltered prediction results.