Prediction Method of Ocean Wave Spectrum Based on an Echo State Network

Abstract

Zhang, X.; Yang, B.; Sun, H., and Zhang, S., 2020. Prediction method of ocean wave spectrum based on an echo state network. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 319–323. Coconut Creek (Florida), ISSN 0749-0208.In order to realize the prediction of ocean wave spectrum under strong nonlinear conditions, a new method combining the empirical mode decomposition (EMD) and an echo state neural network (ESN) is proposed based on ocean wave spectral data measured by shipborne X-band radar. The proposed method uses empirical mode decomposition to decompose the time series of the strongly nonlinear wavelet spectral density values so that the nonlinearity and nonstationarity of the obtained subsequence is greatly reduced compared with the original sequence, and the echo state neural network can be applied separately. By superimposing the prediction results, the predicted values of the wavelet spectral density can be obtained, and the entire ocean wave spectral information can be further combined. The results show that the method can effectively solve the problem of poor prediction effects under strong nonlinear conditions. The method can provide a certain basis for ships to obtain wave spectral information in real time and improve the practicability of shipborne wave radar systems.