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

Research on intelligent prediction of navigation situation based on ship operation data
Xiang Fan, Tongwei Shen, Wenyu Shi, Yao Qin, Li Chen
Shanghai Merchant Ship Design and Research Institute, Shanghai, China

ABSTRACT
Navigation situation prediction is of great significance in manyscenarios. It will help improving efficiency and security in collision
avoidance, berthing and unberthing and so on. In this paper, a Long
Short-Term Memory (LSTM) neural network is applied to predict thenavigation situation of a large merchantman, which includes
longitudinal speed and heading angle. The operation data are on-line
measured, transmitted, fused and filtered by the digital operation
support system (DOSS®). Data of six voyages in 18 months were used
for model training. By comparing with the target value, the accuracy ofthe prediction model is verified. Further, the effect of prediction time
length and input error on the predicted results are analyzed. It is foundthat with the increase of prediction time length, the deviation between
predicted results and actual values also increases. There is an obvious
linear relationship between the change rate of prediction and the change
rate of input. Compared with heading angle, the accuracy of predicted
speed is more sensitive to the prediction time length and its
measurement error.

wave and current. The numerical solution method was also given in
their paper. Peng et al. (2019) developed a modified unscented Kalmanfilter (MUKF) and applied it in estimating dynamic positioning ship
motion states. The results pointed out that MUKF showed a goodstability and convergence performance based on the parameter
sensitivity analysis. Ma et al. (2006) predicted ship`s pitch motion
based on autoregressive (AR) method. Jiang et al. (2020) studied theship hull scale effect of real-time motion prediction using AR model. It
showed the AR model performance was better for ships with larger
principal dimensions where ship hulls were the same.
With the development of artificial intelligence (AI) technology, variousmachine learning and depth learning method were applied in navigation
situation prediction. Xu et al. (2021) combined sliding time window
with least squares support vector machine (SVM) to solve higher ordernonlinear rolling problem. The simulated data was generated by fourth
Runge-Kutta method and the super parameters of the model wereoptimized online by automatic grid search method. Based on long–short-term-memory (LSTM) deep neural network, Jiang et al. (2021)
proposed a novel system identification scheme to obtain a MIMO
model of ship maneuvering motion, which can leverage the temporalcorrelation from the constructed training data to learn the underlying
feasible model robust to extraneous noise. The results showed that thecomplex nonlinear features of ship maneuvering motion can be learned
from the measured temporal data, using standard training techniques fordeep neural networks. Yin et al. (2013) applied sequential learning
radial basis function (RBF) neural networks to do on-line prediction
work of ship rolling during maneuvering. Results demonstrated its
effectiveness and efficiency in on-line identification and predictionapplications. Zhang et al. (2021) proposed a multiscale attention-based
LSTM model for ship motion prediction. Under two different seaconditions, the proposed method carried out the prediction experimentsof ship roll, pitch, yaw, sway, surge and heave at 1s intervals,respectively. Compared with many other methods, this model canprovide the better prediction performance in complex sea conditions.

KEY WORDS: Navigation situation, intelligent prediction, operation
data.
INTRODUCTION
Ships sailing at sea are not only affected by their own propulsion andsteering systems, but also disturbed by the marine environment such aswind, wave and current. The effect of marine environment causes the
ship's large amplitude oscillation motion, which leads to leeway andincreases the hidden danger of navigation safety. Therefore, it isnecessary to establish a short-term prediction model of navigation
situation to provide auxiliary decision-making for operation scenarios
such as collision avoidance, berthing and unberthing.
Ship motion has the characteristics of nonlinearity, irregularity anduncertainty. Several methods were proposed to meet the needs ofdifferent motion prediction application scenarios. Hou et al. (2015,2016) established a nonlinear rolling motion equation and applied thesupport vector regression (SVR) to identify the damping and restoring
moment parameters. The rolling motions in regular and irregular wave
are both predicted accurately. Jiang et al. (2010) investigated the heaveand pitch motions of trimaran by using the three-dimensional frequency
domain Green's function method. Liu et al. (2018) proposed amathematical model of ship drift motion considering the effect of wind,

The navigation situation prediction technology based on AI algorithm
is applying in real ships. Yin et al. (2018) used wavelet transform andvariable RBF network to real-time predict the roll motion of Dalian
Maritime University`s research and training ship "YuKun". The
research results showed that the proposed method can be applied topredict the ship roll motion accurately and effectively. Nie et al. (2020)
developed a model for short-term predicting ship motion by combining

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