Significant Wave Height Prediction based on Wavelet Graph Neural Network
This work addresses ocean forecasting for coastal cities to mitigate social and economic losses, representing an incremental improvement in domain-specific deep learning methods.
The paper tackled Significant Wave Height prediction by proposing a Wavelet Graph Neural Network that integrates wavelet transform and graph neural networks to learn spatial-temporal dependencies, achieving superior performance over numerical, machine learning, and deep learning models.
Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empirical-based or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both short-term and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) approach is proposed to integrate the advantages of wavelet transform and graph neural network. Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction. Experimental results show that the proposed WGNN approach outperforms other models, including the numerical models, the machine learning models, and several deep learning models.