A fast, high-precision deep learning model for regional wave prediction

Abstract

Accurate prediction of regional significant wave height is crucial for safe ship navigation, route planning, and reducing carbon emissions from shipping. Physics-based wave prediction methods involve complex calculations, limiting real-time information provision. Convolutional Neural Networks (CNNs) have gained popularity for wave prediction but suffer from feature loss, positional insensitivity, and poor performance in high sea conditions. This study introduces a Vision Transformer-based regional wave prediction model (VIT-RWP) to address these issues. VIT-RWP utilizes an attention mechanism to extract the wind-wave mapping relationship. It employs convolution and transpose convolution as encoder and decoder, preserving positional information and relative point positions within the region. Evaluation in four sea areas, including comparison with CNN-RWP, demonstrates VIT-RWP's advantages. Pre-training enhances VIT-RWP's predictive accuracy by over 0.5%, surpassing CNN-RWP by over 5%. VIT-RWP maintains accuracy even with wave heights exceeding 5 m. Importantly, it exhibits remarkable robustness when subjected to Gaussian noise in input data. VIT-RWP's consistent performance across diverse seas establishes its efficacy and accuracy as a reliable wave prediction model.