  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 predict the heave and surge motions of a floating semi-submersible
20 to 50 seconds 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.
