Multi-step Ship Roll Motion Prediction Based on Bi-LSTM and Input Optimization

Abstract

Ship roll is a crucial metric in assessing the vessel's safety in offshore operations. This paper investigates input selection for predicting short-term ship roll motion using the Bidirectional Long Short-Term Memory Network (Bi-LSTM) and the Sobol sensitivity analysis of ship roll based on the predicted models. Considering the complexity of the impact of forces, velocities, and positions with six degrees of freedom on ship roll, a data-driven model is established to represent the relationship adequately. Firstly, one-step prediction models with different time intervals are established based on Bi-LSTM to express the relationship between all input features and output. Afterward, the Sobol sensitivity analysis is carried out to evaluate the impact of input features on the output based on the predicted models. Finally, mathematical statistics are utilized to optimize input selection for multi-step prediction models by analyzing the sensitivity results. The experimental results demonstrate that optimizing the input feature dimensions can improve the accuracy of one-step, five-step, and ten-step prediction models.