Preventing ship-related environmental pollution is one of the most important concerns of the maritime industry. This research developed a machine learning-based model to detect pollution prevention deficiencies in detained ships to address critical environmental regulations and enhance maritime safety. To develop predictive model, the Random Forest algorithm was performed on the port state control report of 4056 detained ships in the Tokyo MoU region between 2019 and 2023. Quantitative analysis revealed that the port state conducting the inspection was the most impactful variable on the model, affecting its accuracy significantly due to diverse enforcement standards. Deficiency number was the second important variable. The analysis also revealed that ship type and size had above-average effect on the model. The Random Forest model achieved the highest accuracy of 72.43 % compared to other algorithms. Following the predictive modeling, Association Rule Mining was utilized to uncover and delineate implicit relationships among the identified deficiency areas, thus deepening our analysis of pollution prevention strategies. The Random Forest algorithm has high accuracy and robustness in models developed for big data and is resistant to overfitting, and the Apriori algorithm can extract hidden and complex relationships in the data faster and more easily interpretable were the main reasons for the selection of these algorithms. The Apriori algorithm analysis identified critical deficiency areas linked to pollution prevention—namely fire safety, certificate/documentation, ISM, emergency systems, and life-saving appliances—with these areas appearing in most of the rules with high metric values, thus highlighting common compliance gaps. The findings can help port authorities and port state control officers prioritize inspections of ships most likely to be non-compliant, thereby effectively reducing pollution prevention risk and inspection times and costs. Therefore, the measures to be taken may prevent ship-related pollution and achieve the United Nations Sustainable Development Goals, especially related to marine resources.
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