Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (4): 516-522.doi: 10.16183/j.cnki.jsjtu.2021.088

• Transportation Engineering • Previous Articles     Next Articles

Rogue Wave Prediction Based on Four Combined Long Short-Term Memory Neural Network Models

ZHAO Yong(), SU Dan   

  1. Naval Architecture and Ocean Engineering College, Dalian Maritime University,Dalian 116026, Liaoning, China
  • Received:2021-03-18 Online:2022-04-28 Published:2022-05-07


In order to improve the prediction accuracy of rogue waves of the long short-term memory (LSTM) neural network, prediction methods of LSTM with convolution neural networks (CNN), empirical mode decomposition (EMD), auto-aggressive integrated moving averagel (ARIMA) model, and Kalman filtering (KF) were studied. Based on the experimental data of the rogue waves of two single-peak and one three combined peaks, prediction models were established and predicted by data normalization, model parameter optimization and error evaluation. The results show that the prediction accuracy of the four combined models is significantly improved in all the three studied conditions, and the combination with the convolutional neural network has the highest prediction accuracy. The combined models provide a feasible scheme for improving the prediction accuracy of freak waves.

Key words: rogue wave, long short-term memory (LSTM), convolutional neural network (CNN), empirical mode decomposition (EMD), auto-aggressive integrated average (ARIMA), Kalman filtering (KF)

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