Huber SVR-Based Hybrid Models for Significant Wave Height Forecasting Using Buoy Sensors

Abstract

Ocean buoy sensors stand out as the most reliable means of recording Significant Wave Height (SWH). However, accurate forecasting of SWH is still challenging in energy production. To circumvent such a challenge, this work introduces a Huber Support Vector Regression (SVR) based wave hybrid model for short-term Significant Wave Height (SWH) forecasting using data collected from the buoy sensors. The model employs three distinct signal techniques, including wavelet decomposition, empirical mode decomposition, and variational mode decomposition, along with particle swarm optimization to fine-tune the parameters of the Huber SVR model. Our extensive experimental analysis encompassed six cases from buoys located at four different geographical regions. We compared the Huber SVR-based wave hybrid models with twelve alternative models incorporating�?�-SVR, LS-SVR, or LSTM models with different decomposition methods. The results of our numerical experiments demonstrate that the Huber SVR-based wave hybrid models yield superior SWH predictions compared to the other existing models.