Ship Detention Prediction Based on Ensemble Learning Method

Port State Control (PSC) is of positive significance for strengthening ship safety, reducing pollution, and urging the implementation of conventions, and plays an important role in the shipping industry. Detention, as an option of port state inspection, is used to promote the compliance of ships with standards, but for shipping companies, it means economic losses caused by delays in shipping schedules. This study aims to use the ensemble learning method to predict the possible defect types and detention possibilities of the main operating ship types based on the existing PSC inspection data. Based on using historical inspection records to extract characteristic indicators, the stacking algorithm is used to construct a ship detention prediction model, and the accuracy and effectiveness of the model are verified by comparing with the existing main prediction models. The results show that the model proposed in this study can effectively predict ship defects and detention; meanwhile, there are obvious differences in the types of defects that may exist in different ship types. This study has reference value for improving the efficiency of port state inspection and strengthening ship management.