Wavelet-Based ResNet: A Deep-Learning Model for Prediction of Significant Wave Height

Abstract

Predicting significant wave height (SWH) is significant for coastal energy evaluation and utilization, port construction, and shipping planning. It has been reported that SWH is difficult to forecast for the complex marine conditions and chaos in nature. Current methods either require reliable prior information or reach the upper limit of prediction accuracy. To this end, this paper proposes a wavelet-based residual network to predict SWH with high accuracy. First, the time-series data of wave-related factors collected by the ocean buoy station is decomposed using the wavelet transformation. Then, the transformation results are used as the inputs to train the residual neural network. Finally, the data obtained from the NOAA’s National Data Buoy Center is used to prove the outperformed prediction accuracy of the proposed method. The analysis results suggested that wavelet transformation can improve the prediction performance of the neural network, and the proposed model achieves better performance compared with several other deep neural network schemes.

Keywords

• Convolution network
• data mining
• ocean wave time series
• ResNet
• wavelet decomposition