Multi-direction prediction based on SALSTM model for ship motion

Abstract

This paper proposes a self-attention LSTM (SALSTM) model for ship motion prediction, which combines the advantages of LSTM and self-attention mechanisms. The model also introduces the concept of attention gate. The paper studies the influence of forecast lead time on the prediction accuracy of three degrees of freedom: roll, surge and heave. The paper compares the SALSTM model with a baseline LSTM model on a ship motion data set under different forecast durations and lead times. The paper evaluates the performance of the SALSTM model using four metrics and verifies its effectiveness under three representative working conditions. The paper also gives the applicable conditions of the SALSTM model for ship motion prediction