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

Ziyu Zhang; Fei Luo

doi:10.1117/12.3049501

8 November 2024 Multisource data-driven visibility prediction: integrating channel attention mechanism with multiscale temporal convolution

Ziyu Zhang, Fei Luo

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161D (2024) https://doi.org/10.1117/12.3049501
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ziyu Zhang and Fei Luo "Multisource data-driven visibility prediction: integrating channel attention mechanism with multiscale temporal convolution", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161D (8 November 2024); https://doi.org/10.1117/12.3049501

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