Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Application of Machine Learning for Prediction of Wave-Induced Ship Motion
Yangjun Ahn
School of AI Convergence, Sungshin Women’s University
Seoul, Korea
Jae-Hoon Lee, Yonghwan Kim
Department of Naval Architecture & Ocean Engineering, Seoul National University
Seoul, Korea

show better performance than the classical approaches while theiraccuracy and variability are still controversial.

ABSTRACTThe main objective is to investigate the possibility that machinelearning can be used in the real-time simulation of ship motion. A
short-term prediction of seakeeping and maneuvering is strongly
required for the navigation process. However, accurate and instantprediction remains still challenging. In the present study, a physicsbased machine learning model has been proposed. Two physicalattributes that define the ship motion have been learned by applying amachine learning model suitable for each character and integratingthem. The integrated machine learning model has successfully learned
the motion characteristics of the vessel.

Machine learning has been recently used for ship motion prediction
motivated by the effort to apply machine learning to varioushydrodynamic problems (Chen and Chen, 1993). Ferrandis et al. (2021)
and D’Agostino et al. (2021) show that the recurrent neural network(RNN) model can predict the motion time signals based on the CFD
simulation by comparing their time signals. Li et al. (2017) used thecollected sensor data on a vessel to show the feasibility of the neuralnetwork model to predict ship motion under the mild sea states. Liu etal. (2020) studied the required motion records for the recurrent neuralnetwork model to consider the memory effects. Sun et al. (2021)
provide a hybrid model of the recurrent neural network and GaussianProcess Regression to describe the reliability of the prediction. It has
been shown that the database could help to provide nonlinear
characteristics of the vessel (Xu, et al., 2021). Although many previous
studies have shown the possibility that machine learning can be asolution for real-time ship motion prediction, it is hard to confirm thatthose machine learning models have fully learned the physical motion
characteristics of the vessel.

KEY WORDS: Seakeeping; machine learning; impulse response
function; neural network; real-time prediction.

INTRODUCTION
An autonomous ship has been a contentious issue for the shipbuilding
industry. A short-term prediction of seakeeping and maneuvering in thereal-time simulation is strongly required for the navigation process toimprove safety and efficiency. However, accurate prediction remains
still challenging owing to the expensive computation cost of thenumerical simulation for the ship motion (Weymouth et al., 2013).
Existing methods are difficult to respond immediately to the provided
information during a voyage.

Two physical attributes mainly determine the ship motion, but previous
studies did not differentiate them and used only single neural networkmodel such as the RNN. In the present study, a physics-based machinelearning model has been proposed. The numerical model contains twofeatures that can be classified as different physical characteristics of theinstant stimulus derived by the wave force and memory effects. These
different physical attributes that define the ship motion have beenlearned by applying a machine learning model suitable for eachcharacter and integrating them. The integrated machine learning model
has been trained based on a database of the numerical simulation. AKVLCC2 tanker model is used for the numerical simulation as a 6DoF
time-domain model. The Salvesen-Tuck-Faltinsen (STF) method isused for the linear radiation and diffraction forces. The memory effectsof ship motions are modeled by using the impulse response function(IRF) method.

Real-time prediction of ship motions has been studied based on linearhydrodynamic equations, time series prediction models, and machinelearning models (Ferrandis, et al., 2019; Liu et al., 2020). The
prediction of the linear hydrodynamic equation is difficult to estimate
the ship hydrodynamic coefficients that are time-varying in severe
environmental conditions. The prediction of the time series models also
faces difficulties in nonlinear ship motion modeling. Several alternate
methods based on the auto-correlation function proposed by Nielsen etal. (2018) and the auto-regressive moving average by Yumori (1981)

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