Research on Deep Learning-Driven Sea Ice Prediction for Arctic Sea Routes

The strategic development of Arctic sea routes is gaining momentum due to global warming and increasing shipping demands. However, large-scale utilization remains constrained by the high uncertainty associated with dynamic sea ice changes. Traditional sea ice prediction methods struggle to address the rapidly evolving non-equilibrium environment in the Arctic and often fail to accurately capture the complex mechanisms underlying sudden fluctuations in ice conditions and extreme events. This study explores the innovative potential of deep learning technology in sea ice prediction, outlines its advantages and limitations for Arctic sea ice forecasting, highlights the importance of deep learning in integrating observational and simulation data across different spatiotemporal scales, and comparatively evaluates the superiority of integrated deep learning models for Arctic sea ice prediction. Looking ahead, deep learning holds significant promise for intelligent decision-making in Arctic shipping. By enhancing the accuracy of sea ice forecasts, it enables autonomous optimization of sea routes and efficient allocation of icebreaker resources, thereby providing robust scientific support for transforming Arctic sea routes from climate-driven opportunities into technologically advanced infrastructure.