A MMEM-BN–Based Analyzing Framework for Causal Analysis of Ship Collisions

Abstract

Exploring the causes and evolution of ship collisions plays an important role in accident prevention and risk control. This paper proposes a hybrid analyzing framework based on man–machine–environment–management (MMEM) and a Bayesian network (BN), and applied the analyzing framework to causal analysis of ship collisions in Zhejiang coastal waters based on 107 cases of ship collision accidents. The MMEM frame is utilized to guide influencing factors (IFs) and results of IF identification and causal chain analysis are used as basis for structuring the BN model, using 1 and 0 to indicate whether an IF occurs or not, respectively, in regard to an accident. This marking method was used to deal with each of 107 accident cases in order to form set of sample data, of which 97 cases were used as training cases, 10 cases were used as testing cases. The model was trained by using the set of sample data and the expectation maximization (EM) algorithm to obtain the conditional probability tables (CPTs). A 20-node BN model with the ability to predict the probability of occurrence of ship collision was established. The BN model was verified through sensitivity analysis–based validation, k-folds cross-validation, and testing cases–based validation. The validation results show the correctness and usability of the model. Through backward reasoning–based analysis, maximum likelihood cause chain analysis, and sensitivity analysis, it was found that human error (H) is the main IF resulting in ship collisions; the causal chain that maximizes the likelihood of an accident occurring is H1 (Improper lookout) → H4 (Underestimation of collision) → H7 (Failure to take effective collision avoidance measures) → H (Human error) → C (Ship collision). H1, H7, and H9 (Improper emergency handling) have relatively high sensitivity and greater impact on collision accidents. The results show that the proposed MMEM-BN–based analyzing framework is applicable. The analyzing framework and results of causal analysis will provide theoretical and practical supports for exploring origin of accidents, revealing evolutionary mechanism of accidents and for taking targeted risk control measures. Ship collision is one of the most serious marine accidents. When it occurs, it is likely to lead to serious casualties and economic losses. This study analyzed the causes and evolution of ship collision accidents to provide reference for accident prevention. The paper proposes a man–machine–environment–management and Bayesian network (MMEM-BN)–based analyzing framework (work flow) for causal analysis of ship collisions. Using the analyzing framework, influencing factors of ship collisions in Zhejiang coastal waters were identified under the MMEM framework and a BN model of ship collisions was established. Analysis of the results showed that human factors are the key causes resulting in collision accidents, and improper emergency handling or Failure to take effective collision avoidance measures have a greater impact on collision accidents. The proposed MMEM-BN–based analyzing framework was verified to be applicable. The proposed analyzing framework and causal analysis of ship collisions in Zhejiang coastal waters will provide theoretical and practical support for exploring the origin of accidents, determining the evolutionary mechanism of accidents, and taking targeted risk control measures.