# A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States

arXiv: https://arxiv.org/abs/2207.12673 · HTML: https://mlsanity.com/paper/2207.12673/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states](https://mlsanity.com/paper/1912.13382/) (2019)
- [Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State](https://mlsanity.com/paper/2105.13102/) (2021)

## Built on by (most cited first, 3 of 3)

- [Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method](https://mlsanity.com/paper/2405.08033/) (2024)
- [Learning Response-Statistic Shifts and Parametric Roll Episodes from Wave--Vessel Time Series via LSTM Functional Models](https://mlsanity.com/paper/2603.24431/) (2026)
- [Reliability-aware short-term roll prediction for unmanned surface vehicles via multi-task learning and adaptive centralization](https://mlsanity.com/paper/2610.00996/) (2026)

## Abstract (arXiv)

Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is proposed that combines long short-term memory (LSTM) and convolutional neural network (CNN) in parallel. The motivation is to extract the nonlinear dynamic characteristics and the hydrodynamic memory information through the advantage of CNN and LSTM, respectively. For the feature selection, the time histories of motion states and wave heights are selected to involve sufficient information. Taken a scaled KCS as the study object, the ship motions in sea state 7 irregular long-crested waves are simulated and used for the validation. The results show that at least one period of roll motion can be accurately predicted. Compared with the single LSTM and CNN methods, the proposed method has better performance in predicting the amplitude of roll angles. Besides, the comparison results also demonstrate that selecting motion states and wave heights as feature space improves the prediction accuracy, verifying the effectiveness of the proposed method.