The Application of Automatic Identification System Information and PSO-LSTM Neural Network in CRI Prediction

Abstract

Considering that collision accidents happen sometimes, it is necessary to predict the collision risk to ensure navigation safety. With the information construction in maritime and the popularity of automatic identification system application, it is more convenient to obtain ship navigation dynamics. How to obtain ship encounter dynamic parameters through automatic identification system information, assess ship collision risk, find out dangerous target ships, and give early warning and guarantee for ship navigation safety, is a problem that scholars have been studying. As an index to measure the degree of ship collision risk, CRI, namely, collision risk index, is usually obtained by calculating ship encounter parameters and comprehensive analysis. There are many factors that affect CRI, and the values of many parameters depend on expert judgment. The corresponding CRI has nonlinear and complex characteristics, which is highly correlated with the time sequence. In order to enhance the prediction accuracy and efficiency, PSO-LSTM neural network is applied in the paper to predict CRI. Experiments show that PSO-LSTM neural network can effectively predict collision risk and provide a reference for navigation safety.

Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM

“…To address the problem of low accuracy and high complexity in models for ship trajectory estimation and collision risk evaluation, Abebe et al [54] proposed a hybrid Autoregressive Integrated Moving Average-Long Short-Term Memory (ARIMA-LSTM) model based on AIS data. Zhou et al [55] proposed the introduction of a particle swarm optimization (PSO) algorithm to optimise the LSTM model and determine a better prediction of collision risk. The results showed that LSTM based on a particle swarm optimization (PSO-LSTM) model can handle complex problems with higher accuracy.…”