A Sea State Estimation and Uncertainty Awareness Model Integrating Multi-Scale Convolution and DropKey-Transformer

Accurate sea state estimation is of great significance to marine engineering safety and disaster prevention and mitigation. This paper proposes a new sea state estimation architecture integrating multi-scale 1D-CNN, transformer encoder, and the MC-DropKey mechanism. First, the front-end multi-scale 1D-CNN is used in sequence modeling to extract high-quality local features. Then, the back-end transformer encoder is used to capture global long-range temporal dependencies. In addition, the MC-DropKey mechanism is introduced. It achieves dynamic uncertainty quantification while maintaining high-precision estimation. Comprehensive comparative and ablation experiments show that the proposed model significantly outperforms the baseline models in wave height and wave direction estimation tasks, achieving the lowest MAE of 0.117 m and CAE of 5.212°. Moreover, the uncertainty intervals output by the model effectively quantify estimation confidence, reaching a 96.67% prediction interval coverage probability. The obtained results provide strong technical support for scientific scheduling and decision-making in complex marine environments.