Multisource data-driven visibility prediction: integrating channel attention mechanism with multiscale temporal convolution

Visibility prediction is crucial for traffic safety and aerospace, especially under conditions of low visibility caused by fog, smoke, rain, and snow, which significantly affect visual range and driving safety. Accurate visibility prediction helps prevent accidents. However, traditional numerical weather forecasts exhibit low accuracy at small scales and are highly sensitive to initial conditions. Existing deep learning methods primarily focus on temporal changes. To address these challenges, this paper proposes the MSC-CGCRN model for visibility prediction, a graph neural network that integrates multi-scale temporal convolution and channel attention mechanisms. By integrating satellite data and ground meteorological station data, this model utilizes multi-source data for predictions. Experimental results demonstrate that the MSC-CGCRN model outperforms other benchmark models, and ablation experiments confirm the effectiveness of its components.