Sine-SSA-BP Ship Trajectory Prediction Based on Chaotic Mapping Improved Sparrow Search Algorithm

Objective: In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks. Method: First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds. Result: The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature. Conclusions: The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory.

Automated summary

In this study, a Sine chaos mapping-based improved sparrow search algorithm (SSA) is proposed to optimize the BP neural network for trajectory prediction of inland river vessels. The Sine-SSA-BP model effectively improves the initialized population of uniform distribution and reduces premature convergence. The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability compared to conventional LSTM and SVM, especially in predicting corners, which aligns well with real ship navigation trajectories.