Researchers typically resort to numerical methods to understand and predict ocean dynamics, a key task in mastering environmental phenomena. Such methods may not be suitable in scenarios where the topographic map is complex, knowledge about the underlying processes is incomplete, or the application is time critical. On the other hand, if ocean dynamics are observed, they can be exploited by recent machine learning methods. In this paper we describe a data-driven method to predict environmental variables such as current velocity and sea surface height in the region of Santos-Sao Vicente-Bertioga Estuarine System in the southeastern coast of Brazil. Our model exploits both temporal and spatial inductive biases by joining state-of-the-art sequence models (LSTM and Transformers) and relational models (Graph Neural Networks) in an end-to-end framework that learns both the temporal features and the spatial relationship shared among observation sites. We compare our results with the Santos Operational Forecasting System (SOFS). Experiments show that better results are attained by our model, while maintaining flexibility and little domain knowledge dependency.

在本文中，我们使用先进的机器学习方法，包括序列模型（LSTM和Transformers）和关系模型（图神经网络），建立了一个在巴西东南海岸Santos-Sao Vicente-Bertioga海湾地区可以预测洋流速度和海面高度等环境变量的数据驱动模型，并将我们的结果与Santos操作预测系统进行了比较，证明了我们的模型可以在保持一定的灵活性和少量领域知识依赖性的同时实现更好的预测效果。

动态图神经网络建模海洋变量