Evaluation of pollution prevention related deficiencies of ships using association rule mining

Since marine and environmental pollution is a major problem for the maritime industry, preventive implementations are constantly being developed. In this context, this research aimed to determine the dominant factors in ships detected to have pollution prevention deficiencies in port state control (PSC). A total of 12,530 PSC reports carried out by Paris Memorandum of Understanding (MoU) region between 2017 and 2023 were analyzed with the association rule mining. The Apriori algorithm was performed to reveal hidden and meaningful relationships in the inspections. The dominant variables for inspections that detected pollution prevention deficiencies were ship flag, classification society, number of deficiencies, and inspection type. Association rules revealed that pollution prevention deficiency areas differed interestingly according to geographical region, classification society, and ship age. The findings may be a guide for stakeholders for pollution prevention during ship inspections, and contribute to the achievement of maritime-related Sustainable Development Goals (SDGs).