Probabilistic prediction of the heave motions of a semi-submersible by a deep learning model

The real-time motion prediction of a floating offshore platform refers to forecasting its motions in the following one- or two-wave cycles, which helps improve the performance of a motion compensation system and provides useful early warning information. In this study, we extend a deep learning (DL) model, which could give deterministic predictions about the heave motion of a floating semi-submersible 20 to 50 s ahead with good accuracy, to quantify its uncertainty of the predictive time series with the help of the dropout technique. By repeating the inference several times, it is found that the collection of the predictive time series is a Gaussian process (GP). The DL model with dropout learned a kernel inside, and the learning procedure was similar to GP regression. Adding noise into training data could help the model to learn more robust features from the training data, thereby leading to a better performance on test data with a wide noise level range. This study extends the understanding of the DL model to predict the wave excited motions of an offshore platform. • A deep learning model integrated with dropout was developed for the probabilistic prediction of heave motions of a semi-submersible. • The output collection of the predictive time series was identified as a Gaussian process. • The learning procedure is a type of kernel method. • Adding noise into training data helps the model to learn more robust features.