用视觉变压器+GRU预测海面流速，提升航海与环境监测精度
SEA-ViT: Sea Surface Currents Forecasting Using Vision Transformer and GRU-Based Spatio-Temporal Covariance Modeling
- 融合ViT与双向GRU捕捉时空相关性
- 基于30年高频雷达数据，预测海面流速（U, V）
- 适配气候周期，助力泰国海域海洋预报
海面流预测对航海导航、环境监测和气候分析至关重要，尤其在泰国湾和安达曼海等区域。本文提出SEA-ViT，一种结合视觉变换器（ViT）与双向门控循环单元（GRU）的深度学习模型，利用高频雷达（HF）数据预测海面流速（U, V），通过捕捉时空协方差实现高精度预测。该模型基于覆盖30余年的丰富数据集，并引入厄尔尼诺（El Niño）、拉尼娜（La Niña）及中性相位的ENSO指数，以揭示地理坐标与气候变率之间的复杂关系。此方法显著提升了海面流预测能力，支持泰国地球信息与空间技术发展局（GISTDA）在海洋区域的应用需求。代码与预训练模型已公开于https://github.com/kaopanboonyuen/gistda-ai-sea-surface-currents。
原文摘要 · Abstract (English)
Forecasting sea surface currents is essential for applications such as maritime navigation, environmental monitoring, and climate analysis, particularly in regions like the Gulf of Thailand and the Andaman Sea. This paper introduces SEA-ViT, an advanced deep learning model that integrates Vision Transformer (ViT) with bidirectional Gated Recurrent Units (GRUs) to capture spatio-temporal covariance for predicting sea surface currents (U, V) using high-frequency radar (HF) data. The name SEA-ViT is derived from ``Sea Surface Currents Forecasting using Vision Transformer,'' highlighting the model's emphasis on ocean dynamics and its use of the ViT architecture to enhance forecasting capabilities. SEA-ViT is designed to unravel complex dependencies by leveraging a rich dataset spanning over 30 years and incorporating ENSO indices (El Niño, La Niña, and neutral phases) to address the intricate relationship between geographic coordinates and climatic variations. This development enhances the predictive capabilities for sea surface currents, supporting the efforts of the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand's maritime regions. The code and pretrained models are available at \url{https://github.com/kaopanboonyuen/gistda-ai-sea-surface-currents}.
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