Machine Learning for Short-Term Prediction of Ship Motion Combined with Wave Input

Abstract

There is a response relationship between wave and ship motion. Based on the LSTM neural network, the mapping relationship between the wave elevation and ship roll motion is established. The wave elevation and ship motion duration data obtained by the CFD simulation are used to predict ship roll motion with different input data schemes. The results show that the prediction scheme considering the wave elevation input can predict ship roll motion. Compared with the direct prediction scheme based on the roll data input, the prediction scheme considering the wave elevation input factor can greatly improve the prediction accuracy and effective advance prediction time. Different wave elevation data inputs have different prediction effects. The advance prediction duration will increase with the increase in the input wave elevation position and the ship distance. The simultaneous input of multi-point wave elevation greatly increases the amount of data, allowing the trained model to utilize a greater data depth. This not only improves the advance prediction duration of the prediction model, but it also enhances the robustness of the model, making the prediction results more stable.

Keywords

• ship motion
• random wave
• machine learning
• short-term prediction