An ensemble multi-step forecasting model for ship roll motion under different external conditions: A case study on the South China Sea. (30th September 2022)

• Record Type: Journal Article
• Authors: Wei, Yunyu; Chen, Zezong; Zhao, Chen; Tu, Yuanhui; Chen, Xi; Yang, Rui
• Is Part Of: Measurement. Volume 201(2022)
• Publication Date: 2022-09-30
• Subjects: Ship roll prediction -- Adaptive secondary decomposition -- Deep belief network -- Multi-input multi-output strategy -- Multi-objective optimization -- Adaptive error correction; ASD adaptive secondary decomposition -- DBN deep belief network -- MIMO multi-input multi-output -- AEC adaptive error correction -- DWT discrete wavelet transform -- EWT empirical wavelet transform -- IEWT inverse empirical wavelet transform -- VMD variational mode decomposition -- SRUN simple recurrent unit network -- BPNN back propagation neural network -- LSTM long-short term memory -- MOICA multi-objective imperialist competitive algorithm -- MOJS multi-objective jellyfish search -- ELM extreme learning machine -- SARIMA Seasonal Auto Regressive Integrated Moving Average -- SD standard deviation -- MSE mean square error -- ARIMA Autoregressive Integrated Moving Average -- SampEn Sample Entropy -- AM-FM amplitude modulation-frequency modulation -- RBM Restricted Boltzmann Machine -- BP Back Propagation -- MLE maximum likelihood estimation -- CD contrastive divergence -- AR autoregressive -- MA moving average -- AIC Akaike Information Criterion -- BIC Bayesian information criterion -- Hs significant wave height -- Wd wave direction -- Tav average wave period -- MAE mean absolute error -- MAPE mean absolute percentage error -- RMSE root-mean-square error -- BiLSTM bidirectional long-short term memory -- DBN-R DBN model under recursive strategy -- DBN-M DBN model under MIMO strategy -- FA firefly algorithm -- PSOGSA particle swarm optimization and gravitational search algorithm -- GSA gravitational search algorithm -- PSO particle swarm optimization; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111679
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Propose a novel DBN ensemble model for ship roll multi-step forecasting under different external conditions. Reduce the error accumulation of multi-step prediction by the DBN model under the MIMO strategy. Obtain the optimal hyperparameters of the DBN model by the MOJS algorithm. Reduce nonlinear and nonstationary of ship roll data by the adaptive secondary decomposition method. Correct the initial prediction error by the adaptive error correction method. Abstract: The external environment is the main factor affecting the stability of ship roll motion. Accurate forecasting of ship roll motion under different external conditions can help assure navigational safety and increase ship operating efficiency. In this study, an ensemble multi-step forecasting model for ship roll motion under different environmental conditions is proposed, which consists of adaptive secondary decomposition (ASD), deep belief network (DBN) under multi-input multi-output (MIMO) strategy, multi-objective optimization, and adaptive error correction (AEC). To evaluate the performance of the proposed ensemble model, five experiments are set up to make the 5-step, 7-step, and 9-step ahead prediction for the ship roll series, respectively. Four different external environment ship roll datasets from the South China Sea in 2020 were employed to validate the robustness of the ensemble multi-step forecasting model. The experimental results demonstrate that the proposed model based on adaptive secondary decomposition, multi-objective optimization, and adaptive error correction can accurately and effectively predict ship roll motion under different external conditions.