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

arXiv: https://arxiv.org/abs/2111.00873 · HTML: https://mlsanity.com/paper/2111.00873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 9 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Deep and Confident Prediction for Time Series at Uber](https://mlsanity.com/paper/1709.01907/) (2017)
- [Deep Learning with Long Short-Term Memory for Time Series Prediction](https://mlsanity.com/paper/1810.10161/) (2018)
- [Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states](https://mlsanity.com/paper/1912.13382/) (2019)
- [Predicting heave and surge motions of a semi-submersible with neural networks](https://mlsanity.com/paper/2007.15973/) (2020)
- [A Bayesian - Deep Learning model for estimating Covid-19 evolution in Spain](https://mlsanity.com/paper/2005.10335/) (2020)

## Abstract (arXiv)

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.