On the K-means clustering model for performance enhancement of port state control

Abstract

Nowadays, the concept of port state control is viewed as a safety net to safeguard maritime security, protect the marine environment, and ensure decent working and living circumstances for seafarers on board to a large extent. The ship can be detained for further checking if significant deficiencies are discovered during a port state control inspection. There is much research on this topic, but there have been few studies on the relationship between ship deficiencies and ship detention decisions using unsupervised machine learning artificial intelligence techniques. Although the previous methods or models are feasible for ship detention decisions, they all have shortcomings to some extent, such as large training model errors caused by the imbalance of class labels in the dataset and the fact that the training model cannot comprehensively consider all factors influencing ship detention decision due to the complexity and diversity of the problem. Unsupervised algorithms do not need to label all data in advance, and we can incorporate some fields related to port state control inspection data that can be collected into the model to allow the computer to automatically classify the ships at different risk levels according to relative criteria, e.g., the Tokyo memorandum of understanding, which may result in more objective results, thus eliminating the influence of subjective domain knowledge. It may also have more comprehensive coverage and more information on port state control inspection and decision models. Therefore, this research explores and develops an unsupervised algorithm based on k-means to improve port state control inspection decision-making models using the six-years inspection data from the Tokyo memorandum of understanding. The results show that the accuracy rate is around 50%.