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Meteorological observation research based on an improved EfficientNetV2 model
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DOI：10.1016/j.envsoft.2025.106835.png)
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Abstract
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• A novel deep learning model named EfficientNetV2-CBAM-PANet is proposed for weather image classification. • The model achieves 97.6% accuracy on a self-built dataset, outperforming 13 baseline and state-of-the-art models. • The proposed model shows fast convergence, strong generalization, and high interpretability in rain, snow, and fog classification.
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