To enhance the SEA-ViT model's forecasting capabilities for sea surface currents, additional environmental and oceanographic variables can be integrated into the existing framework. Here are several strategies for incorporating these variables:
Feature Integration: The model can be extended to include additional input features such as wind patterns, bathymetry, and temperature gradients. For instance, wind speed and direction can be represented as vector components (U_wind, V_wind) and included alongside the existing sea surface current vectors (U, V). Bathymetric data can be encoded as a spatial feature, providing context on how underwater topography influences current patterns. Temperature gradients can be included as an additional feature to capture thermal stratification effects on ocean dynamics.
Multi-Input Architecture: The architecture can be modified to accommodate multiple input streams. For example, separate branches can be created within the SEA-ViT framework to process different types of data (e.g., one for sea surface currents, another for wind patterns, and a third for temperature gradients). These branches can then be fused at a later stage, allowing the model to learn complex interactions between these variables.
Temporal Dynamics: Incorporating temporal dynamics of these additional variables is crucial. For instance, using time-series data for wind patterns and temperature gradients can help the model understand how these factors evolve and influence sea surface currents over time. This can be achieved by applying similar GRU layers to these additional features, capturing their temporal dependencies.
Data Augmentation: To ensure robustness, data augmentation techniques can be applied to the new features. For example, introducing noise or perturbations to wind speed and temperature data can help the model generalize better to unseen conditions.
Physical Constraints: The model can also integrate physical principles governing ocean dynamics, such as the Navier-Stokes equations, to account for the influence of wind and temperature on current behavior. This can be achieved by modifying the loss function to include terms that penalize deviations from expected physical behavior based on these additional variables.
By implementing these strategies, the SEA-ViT model can leverage a richer dataset, leading to improved accuracy in forecasting sea surface currents and better understanding of the complex interactions between various oceanographic factors.