Interpreting spatiotemporal dynamics of Ulva prolifera blooms in the southern yellow sea using an attention-enhanced transformer framework.

Harmful algal blooms dominated by Ulva prolifera have posed recurring ecological and economic challenges in the southern Yellow Sea. To better understand and predict the complex spatiotemporal dynamics of these blooms, we developed an enhanced Transformer-based deep learning framework, incorporating multi-head self-attention mechanisms. This model dynamically captures spatial dependencies, providing a comprehensive understanding of bloom dynamics. Utilizing twelve key marine environmental factors, we systematically explored all possible feature combinations to determine the optimal predictive subset. Experimental results demonstrated superior predictive performance of the model (MAE: 0.0213, MSE: 0.0016, R²: 0.9923) compared to conventional deep learning models and recent spatiotemporal deep learning models. Training dynamics revealed efficient convergence, especially with comprehensive environmental information. Spatial attention analysis revealed that offshore regions consistently received higher attention, indicating their critical role as informative and generalizable environmental references. Furthermore, exhaustive feature attribution experiments identified an optimal combination of eight environmental factors-including temperature, salinity, current velocity, precipitation, wind direction, dissolved iron, phosphate, and silicate-were found to significantly enhance prediction accuracy. This study highlights the capability of attention-enhanced Transformer models for interpretable and precise ecological forecasting, providing valuable insights for targeted mitigation and management of U. prolifera blooms.