Optimized Bi-LSTM Model for Short-Term Predicting of Ship State with Definitions of Surf-Riding and Broaching

Abstract

This paper introduces a hybrid prediction method that combines the Bi-LSTM neural network with definitions of surf-riding, wave-blocking and broaching to enhance the safety and stability of ship navigation. The hybrid method can accurately predict ships’ attitude motions and states by recognizing ship states and encoding them into one-hot representations. The Bi-LSTM model’s bidirectional learning capability captures significant temporal dependencies, enabling precise and timely predictions of complex maritime events across various conditions. Additionally, the direct output approach of state features improves prediction accuracy by eliminating intermediate steps, allowing for the better anticipation of and response to critical events. Validated with ship navigation data from autopilot simulations in wave conditions, the hybrid method outperforms conventional methods based on LSTM and Bi-LSTM models, demonstrating strong generalization capabilities and significantly contributing to safer and more stable ship navigation.