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Anomalous Vessel Behavior Detection Based on SVR Seaway Model
International Journal of Fuzzy Logic and Intelligent Systems 2019;19(1):18-27
Published online March 25, 2019
© 2019 Korean Institute of Intelligent Systems.

Joo-Sung Kim1, Jin-Suk Lee1, and Kwang-Il Kim2

1Division of Navigation Science, Mokpo National Maritime University, Mokpo, Korea, 2Division of Marine Industry and Maritime Police, Jeju National University, Jeju, Korea
Correspondence to: Kwang-Il Kim (
Received February 26, 2019; Revised March 13, 2019; Accepted March 14, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The identification of anomalous behavior of own vessel and its targets is one of the most important task to ensure safety of navigation. In particular, it is essential to determine the anomalous behavior of a ship in the decision-making process. The existing anomalous behavior detection method defines the anomalous behavior by judging the abrupt changes of a ship’s movement. However, the navigational data that observed in actual marine accidents were often showed as a normal condition. It means that if there were persistent differences at certain duration, the accumulated data could become large enough to cause of an accident. In this study, the ship’s anomalous behavior was determined based on the SVR seaway model and its route extraction method. It was intended to propose a method of defining acceptable maximum and minimum values to determine the anomalous behavior by assigning navigational data to the location basis. For the verification of the proposed method, it was constructed that virtual route and targets which are similar to the actual navigational environment. As a result of the simulation, anomaly detection data on the anomalous behavior were presented. It is expected that the proposed method could be a decision-making support tool to mariners and contributes to the reduction of marine accidents related on the anomalous behavior.
Keywords : Vessel Traffic Services, Anomalous behavior, Seaway model, Support vector regression, Machine learning