Abstract:

In response to the frequent and high-impact accidents in key marine areas, a risk assessment model for accidents in such areas based on TAN network was established. To address the issue of partial sample bias in accident reporting, the boxplot method was employed to eliminate outliers and improve data quality. Considering the complexity and correlation of risk factors, a random forest algorithm was utilized to identify key risk factors and establish a risk evaluation index system for accidents in key marine areas. In addition, the performance of TAN network model was compared with six machine learning models for validation and analysis. The results demonstrate that TAN network achieves the highest accuracy of 93.02%. The findings indicate that ship speed, ship length, and pirate attacks are the primary factors contributing to risk events in key marine areas. Vessels aged between 11 and 20 years should be prioritized for maintenance and inspection. In addition, ships navigating in shallow key marine areas should operate with increased caution.

Key words: tree augmented naive Bayesian (TAN) network, key marine areas, maritime accidents, risk assessment, random forest