Severity prediction and risk assessment for non-traditional safety events in sea lanes based on a random forest approach. (15th June 2022) - Lu, Jing; Su, Wan; Jiang, Meizhi; Ji, Yuan
Abstract: Non-traditional safety (NTS) events pose an increasingly serious threat to the security of sea lanes, highlighting the significance of prediction and prevention. In this paper, a random forest (RF)-based approach is provided so as to assess the severity and risk of NTS events in sea lanes. Data was manually collected from the Global Integrated Shipping Information System (GISIS) and first classified into four severity levels, according to the classification criteria from the International Maritime Organization (IMO) Casualties and Incidents reports. The risk factors are determined based on extensive literature review and data analysis. Then, a random oversampling (RO) method is used to process the unbalanced samples before using them as an input in the RF, where they are divided into a training set for rule learning and a test set for verifying the model's validity. Finally, the risk is calculated based on the severity probabilities returned by the RF-RO, and the severity is quantified by expert scoring. The results reveal that the proposed RF-RO performs with the greatest accuracy among similar algorithms. The feature importance measurement indicates that time and ship type are the most influential risk factors in anticipating an NTS. These findings provide a reference for stakeholders in implementing security management.