A BiLSTM hybrid model for ship roll multi-step forecasting based on decomposition and hyperparameter optimization. (15th December 2021) - Wei, Yunyu; Chen, Zezong; Zhao, Chen; Tu, Yuanhui; Chen, Xi; Yang, Rui
Abstract: 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 multi-step 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 °, the mean average error (MAE) is 0.0742 °, 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 °, 0.0742 °, and 0.0991 °, respectively. Highlights: A new multi-step forecasting model for ship's roll motion. Reduce the non-stationary characteristics of the ship's roll data by an adaptive decomposition algorithm. Reduce prediction error by the BiLSTM model under the MIMO strategy. Obtain the optimal hyperparameters by the hybrid PSOGSA algorithm.