期刊
JOURNAL OF MARINE SCIENCE AND ENGINEERING
卷 9, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/jmse9050538
关键词
ship collision risk; support vector machine; relevance vector machine
This study introduces an enhanced machine learning method to estimate ship collision risk, with the relevance vector machine (RVM) showing more accurate and efficient results compared to the conventional support vector machine (SVM). By supporting more reliable decision-making for navigators through precise risk estimation, early evasive actions can be facilitated.
According to the statistics of maritime collision accidents over the last five years (2016-2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators' carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships' parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.
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