A BiLSTM hybrid model for ship roll multi-step forecasting based on decomposition and hyperparameter optimization

The forecasting of ship's roll motion is the key to ensuring the safety of ship surface operations and improving operations efficiency. A new hybrid multi-step forecasting model is proposed in this paper. The proposed model combines three methodologies, including adaptive empirical wavelet transform (EWT), multi-step forecasting under the multi-input multi-output (MIMO) strategy of bidirectional long short-term memory (BiLSTM) model, and hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) hyperparameter optimization. The three sets of ship roll datasets in the South China Sea are selected to verify the performance of the hybrid multistep prediction model. In the end, the results of the research indicate that: (a) The proposed model has a superior prediction accuracy in multi-step prediction, taking dataset #1 as an example, the root mean square error (RMSE) of the prediction result is 0.0934 degrees, the mean average error (MAE) is 0.0742 degrees, and the mean absolute percentage error (MAPE) is 2.9878%; (b) The proposed hybrid multi-step forecasting model is suitable for different datasets and has strong robustness. Taking the 3-step prediction of dataset #1 to #3 as examples, the RMSEs of the proposed model are 0.0879 degrees, 0.0742 degrees, and 0.0991 degrees, respectively.

Automated summary

This study proposes a new hybrid multi-step ship's roll motion forecasting model, which combines three methodologies and demonstrates superior prediction accuracy and strong robustness.