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http://dx.doi.org/10.7837/kosomes.2020.26.2.139

Analysis of Feature Importance of Ship's Berthing Velocity Using Classification Algorithms of Machine Learning  

Lee, Hyeong-Tak (Ocean Science and Technology School, Korea Maritime & Ocean University)
Lee, Sang-Won (Graduate School, Kobe University)
Cho, Jang-Won (Korea Institute of Maritime and Fisheries Technology)
Cho, Ik-Soon (Division of Global Maritime Studies, Korea Maritime & Ocean University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.26, no.2, 2020 , pp. 139-148 More about this Journal
Abstract
The most important factor affecting the berthing energy generated when a ship berths is the berthing velocity. Thus, an accident may occur if the berthing velocity is extremely high. Several ship features influence the determination of the berthing velocity. However, previous studies have mostly focused on the size of the vessel. Therefore, the aim of this study is to analyze various features that influence berthing velocity and determine their respective importance. The data used in the analysis was based on the berthing velocity of a ship on a jetty in Korea. Using the collected data, machine learning classification algorithms were compared and analyzed, such as decision tree, random forest, logistic regression, and perceptron. As an algorithm evaluation method, indexes according to the confusion matrix were used. Consequently, perceptron demonstrated the best performance, and the feature importance was in the following order: DWT, jetty number, and state. Hence, when berthing a ship, the berthing velocity should be determined in consideration of various features, such as the size of the ship, position of the jetty, and loading condition of the cargo.
Keywords
Ship's berthing velocity; Machine learning; Classification algorithm; Feature importance; Confusion matrix;
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Times Cited By KSCI : 6  (Citation Analysis)
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