The prediction of the collision incident level in the lower reaches of the Yangtze River based on the mutual information

In the lower reaches of the Yangtze River, collision is the major type of maritime accidents. The prediction of the collision incident level is significant for reducing the loss of lives and property, decreasing the probability of accidents and assuring the maritime safety. The lower reaches of the Yangtze River is taken as a case. 218 ship collisions accidents happened in 2013 are selected for the study. All the collision accident data is analyzed to extract the influencing factors. In this study, the link between two factors is found by the mutual information calculated by the data, rather than suggestions of experts. While an appropriate estimated threshold value is given, the relationships among factors can be obtained. The parent node and the child node in any connection can be identified based on the goal of the forecasting. Conditional Probability Tables is computed by the data of related influencing factors. A Bayesian network can then be modeled for the prediction of the collision incident level. The verification based on the validation data shows the modeled Bayesian network runs effectively when forecasting. The proposed Bayesian network model can facilitate the supervision of the maritime safety administration and operation of vessels.