Ship pitch prediction method based on LSTMC and multi-head attention

Abstract

When ships sail in the sea, ship motion is unstable and stochasticity due to the ever-changing weather and marine conditions. Aiming to address the issue of low prediction accuracy in traditional prediction methods, short term predictions of ship pitch at 1 step, 3 steps, 6 steps, and 12 steps in advance are conducted, and the impact of input-output ratios on the prediction model is studied. The seven types of data of the marine environment and ship motion are taken as model inputs, with pitch as the model output. When predicting 1 step in advance, a prediction model based on LSTM and multi-head attention (LMA) is proposed. This model firstly uses LSTM layer to extract the temporal feature of the input sequence, and then uses the multi-head attention layer to assign larger weights to the key information to enhance prediction accuracy. When predicting 3 steps, 6 steps, and 12 steps in advance, as the input-output ratio increases, the length of the input sequence keeps growing. The model needs to sufficiently capture the local spatial characteristic and long-range dependency between multi-variable inputs, a deep prediction model based on Conv2d and LMA model is proposed. The model firstly uses two-dimensional convolution layer to extract local spatial feature of long sequences, and then through the attention layer, assign larger weights to temporal information and spatial information that are conducive to continuous prediction in order to enhance prediction accuracy. Research on the input-output ratio indicates that, as the number of predicted steps increases, the appropriate ratio continuously decreases, but the length of the input sequence keeps increasing. Finally, four evaluation metrics are used to evaluate the proposed algorithms on real ship data, and the prediction stability and accuracy of the LMA model and the LCMA model are also verified.