Estimation of vessel collision risk index based on support vector machine

Abstract

Collision risk index is important for assessing vessel collision risk and is one of the key problems in the research field of vessel collision avoidance. With accurate collision risk index obtained through vessel movement parameters and encounter situation analysis, the pilot can adopt correct avoidance action. In this article, a collision risk index estimation model based on support vector machine is proposed. The proposed method comprises two units, that is, support vector machine–based unit for predicting the collision risk index and the genetic algorithm–based unit for optimizing the parameters of support vector machine. The model and algorithm are illustrated in the empirical analysis phase, and the comparison results show that genetic algorithm-support vector machine model can generally provide a better performance for collision risk index estimation. Meanwhile, the result also indicates that the model may be not so good when we take a higher value of collision risk index. So, the distinguishing threshold of collision risk level should be adjusted according to actual situation when applying this model in practical application.