Fang Wang

This study examines the probabilistic patterns associated with casualty severity in collisions between commercial and fishing vessels in China’s coastal waters. Using 137 official accident investigation reports from 2013 to 2022, a structured dataset capturing vessel characteristics, environmental conditions, and human liability factors was constructed. A Tree-Augmented Bayesian Network (TAN-BN) was developed to model the probabilistic interactions among these variables and to identify the key drivers of casualty severity. Sensitivity analysis based on mutual information indicates that fishing vessel length is the most influential factor affecting casualty outcomes (MI = 0.322), followed by time of occurrence, wind speed, visibility, and season. Scenario analysis using MPE indicates that severe casualty scenarios are associated with adverse temporal and environmental conditions such as nighttime, poor visibility, and open-water environments, while liability-specific analysis further shows that collisions attributed primarily to commercial vessel errors are most likely to result in 4–10 casualties. The results highlight the structural vulnerability of small fishing vessels and the critical role of environmental exposure in heterogeneous vessel encounters. This study provides an interpretable probabilistic framework for examining casualty severity patterns in maritime collisions and offers risk-informed insights for improving sustainable maritime safety management in mixed-traffic coastal waters.

This research addresses the critical issue of major fishing vessel accidents, which traditionally suffer from a lack of focused analysis due to their rarity and the subjective nature of their classification. We propose an innovative methodology of Peaks Over Threshold to overcome subjectivity in accident classification. This approach ensures a more representative and accurate analysis of major accidents, distinguishing them from more common, less severe incidents. Employing a Bayesian network model, we further explore the most influential factors contributing to these major accidents. The key innovation lies in our novel approach to data handling and analysis, enabling us to uncover hidden patterns and causal relationships that traditional methods often overlook. The results show that the approach proposed in this study can effectively capture the key factors of major fishing vessel accidents. This study identifies accident type, vessel-related factors, and accident location as the key influential factors leading to major accidents. The findings from our research are intended to inform sustainable fisheries management practices, promoting interventions that aim to decrease the occurrence and impact of severe maritime accidents while balancing economic, safety, and sustainable development considerations.

This research aims to identify and analyze the significant risk factors contributing to accidents involving fishing vessels, a crucial step towards enhancing safety and promoting sustainable practices in the fishing industry. Using a data-driven Bayesian network (BN) model that incorporates feature selection through the random forest (RF) method, we explore these key factors and their interconnected relationships. A review of past academic studies and accident investigation reports from the Fishery Mutual Insurance Association (FMIA) revealed 17 such factors. We then used the random forest model to rank these factors by importance, selecting 11 critical ones to build the Bayesian network model. The data-driven Bayesian network (BN) model is further utilized to delve deeper into the central factors influencing fishing vessel accidents. Upon validation, the study results show that incorporating the random forest feature selection method enhances the simplicity, reliability, and precision of the BN model. This finding is supported by a thorough performance evaluation and scenario analysis.