An EMD–PSO–LSSVM Hybrid Model for Significant Wave Height Prediction

Abstract

The accurate prediction of significant wave height (SWH) offers major safety improvements for coastal and ocean engineering applications. However, the significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction work a non-straightforward task. The aim of the research presented in this paper is to improve the predicted significant wave height via a hybrid algorithm. Firstly, an empirical mode decomposition (EMD) is used to preprocess nonlinear data, which are decomposed into several elementary signals. Then, a least squares support vector machine (LSSVM) with nonlinear learning ability is adopted to predict the SWH, and a particle swarm optimization (PSO) automatically performs the parameter selection of the LSSVM modeling. The results show that the EMD–PSO–LSSVM model can compensate for the lag in the prediction timing of the prediction models. Furthermore, the prediction performance of the hybrid model has been greatly improved in the deep-sea area; the prediction accuracy of the coefficient of determination (R2) increases from 0.991, 0.982, and 0.959 to 0.993, 0.987, and 0.965, respectively. The prediction performance results show that the proposed EMD–PSO–LSSVM performs better than the EMD–LSSVM and LSSVM models. Therefore, the EMD–PSO–LSSVM model provides a valuable solution for the prediction of SWH.