Preprints
https://doi.org/10.5194/os-2021-2
https://doi.org/10.5194/os-2021-2

  28 Jan 2021

28 Jan 2021

Review status: this preprint is currently under review for the journal OS.

An EMD-PSO-LSSVM hybrid model for significant wave height prediction

Gang Tang1, Haohao Du1, Xiong Hu1, Yide Wang2, Christophe Claramunt3, and Shaoyang Men4 Gang Tang et al.
  • 1Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
  • 2Institut d’Électronique et des Technologies du numérique (IETR), UMR CNTS 6164, Polytech Nantes-Site de la Chantrerie, 44306 Nantes, France
  • 3Naval Academy Research Institute, F-29240 Lanvéoc, France
  • 4School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510006, China

Abstract. Accurate and significant wave height prediction with a couple of hours of warning time should offer major safety improvements for coastal and ocean engineering applications. However, significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction simulation a non straightforward task. The aim of the research presented in this paper is to improve predicted significant wave height via a hybrid algorithm. Firstly, empirical mode decomposition (EMD) is used to preprocess the nonlinear data, which are decomposed into several simple signals. Then, least square support vector machine (LSSVM) with nonlinear learning ability is used to predict the significant wave height, and particle swarm optimization (PSO) is implemented to automatically perform the parameter selection in LSSVM modeling. The EMD-PSO-LSSVM model is used to predict the significant wave height for 1, 3 and 6 hours leading times of two stations in the offshore and deep-sea areas of the North Atlantic Ocean. The results show that the EMD-PSO-LSSVM model can remove the lag in the prediction timing of the single prediction models. Furthermore, the prediction accuracy of the EMD-LSSVM model that has not been optimized in the deep-sea area has been greatly improved, an improvement of the prediction accuracy of Coefficient of determination (R2) from 0.991, 0.982 and 0.959 to 0.993, 0.987 and 0.965, respectively, has been observed. The proposed new hybrid model shows good accuracy and provides an effective way to predict the significant wave height for the deep-sea area.

Gang Tang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-2', Anonymous Referee #1, 01 Mar 2021
    • AC1: 'Reply on RC1', Haohao Du, 06 Mar 2021
  • RC2: '关于 os-2021-2 的评论', Anonymous Referee #2, 29 Mar 2021
  • RC3: 'Comment on os-2021-2', Anonymous Referee #2, 30 Mar 2021
    • AC2: 'Reply on RC3', Haohao Du, 30 Mar 2021

Gang Tang et al.

Gang Tang et al.

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Short summary
Accurate and significant wave height prediction with a couple of hours of warning time should offer major safety improvements for coastal and ocean engineering applications. However, significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction simulation a non straightforward task. The aim of the research presented in this paper is to improve predicted significant wave height via a hybrid algorithm.