Ship Pitch Prediction Based on Bi-ConvLSTM-CA Model

Abstract

When a ship is sailing at sea, its pitch angle will be affected by ship motions such as turning angle, relative wind speed, relative wind direction, velocity in surge and velocity in sway of the ship. Due to the randomness of ship motion attitude and the difficulty of capturing the motion rules, traditional machine learning models, statistical learning models and single deep learning models cannot accurately capture the correlation information between multiple variables, which results in poor prediction accuracy. To solve this problem, the bidirectional convolutional long short-term memory neural network (Bi-ConvLSTM) and channel attention (CA) for ship pitch prediction are used to build a Bi-ConvLSTM-CA model in this paper. The Bi-ConvLSTM-CA prediction model can simultaneously extract the time information and spatial information of the ship motion data, and use the channel attention mechanism to process the output of different time steps to obtain the corresponding weight of each channel. Using the weights to do dot product with the output of Bi-ConvLSTM, the resulting attention mechanism output is processed to produce predicted value by the fully connected layer. Compared with other models, the RMSE index of Bi-ConvLSTM-CA model decreased by at least 28.20%; the MAPE index decreased by at least 29.39%; the MAE index decreased by at least 22.68%. The experimental results of real ship data show that the proposed Bi-ConvLSTM-CA model has a significant reduction in mean absolute percentage error (MAPE), mean square error (MSE) and mean absolute error (MAE) compared with other advanced models, which verifies the effectiveness of the Bi-ConvLSTM-CA model in predicting ship pitch angle.