Multivariate Short-Term Marine Meteorological Prediction Model
Jingbo Li, Li Ma, Yang Li, Yingxun Fu, Dongchao Ma · IEEE Transactions on Geoscience and Remote Sensing · 2025
To address the challenges of traditional marine meteorological prediction methods, which struggle to effectively capture intervariable correlations in multivariate time series data and suffer from insufficient prediction accuracy, this article proposes a multivariate short-term marine meteorological prediction model. First, an intelligent marine prediction fusion architecture is constructed, which is well-suited to artificial intelligence (AI) technologies. This architecture optimizes the process of marine meteorological data collection, processing, and analysis, providing a flexible and efficient infrastructure for short-term marine prediction. Second, an influence-based importance attention mechanism for meteorological variables is designed. By exploiting the differences in interactions among meteorological variables, it selects significant attention heads for computation, effectively reducing the model’s computational complexity and enhancing its response speed. Finally, a multivariate dimension prediction method for marine meteorology is proposed. By independently processing the time series of each variable, it enhances the capability to capture interactions among meteorological variables, thus improving the model’s understanding of and predictions for dynamic changes in marine meteorology. The experimental results show that the model can fully capture and analyze the complex relationship between variables in a multivariable marine meteorological environment, effectively improve the accuracy and efficiency of the prediction, and verify its application potential in marine meteorological prediction.