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

TL;DR

This paper develops a deep learning model that predicts the heave and surge motions of a semi-submersible offshore platform 20 to 50 seconds ahead, incorporating uncertainty quantification through dropout, and demonstrates improved robustness with noisy data.

Contribution

It introduces a deep learning approach that not only predicts platform motions but also quantifies uncertainty, linking dropout to Gaussian process behavior, and enhances robustness with noise training.

Findings

DL model predicts motions 20-50 seconds ahead with good accuracy.

Dropout-based inference models predictive uncertainty as a Gaussian process.

Adding noise during training improves model robustness across noise levels.

Abstract

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.