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<p>The recent Artificial Intelligence (AI) boom has reignited interest in utilizing effective Deep Learning (DL) techniques in various domains. There exists proof to suggest that by integrating Machine Learning (ML) based data analysis with prediction and Convolutional Neural Networks (CNN’s) within the weather detection, forecasting pipeline, and weather recommendations can be improved. This paper tried to examine whether DL techniques can complement traditional numerical weather simulations and data aggregation platforms by enhancing image-based classification and forecasting accuracy. The proposed Meteorology-Net utilizes the DL-based 4-CNNs variants (VGG19, MobileNet, ResNet152V2, InceptionV3) for feature extraction using weather images. The dataset comprised 6862 weather images across 11 classes (dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow). Augmentation techniques such as rotation, scaling, and flipping were applied to balance class distribution (~ 1050 - 1100 samples per class) for weather prediction. Based on the attained results the base DCNNs reached the accepted range of accuracy. Hence to improve the performance of 11-class weather prediction the proposed Meteorology-Net integrates decision-level ensemble concatenation with a multi-task learning framework, enabling simultaneous classification of weather phenomena and forecasting of weather cues. Our proposed Meteorology-Net decision-level ensemble outperformed base DCNNs by achieving higher accuracy (0.9798 vs. 0.9773), improved precision and recall, and reduced error rates, demonstrating superior robustness and generalization for multi-class weather classification. We hope that this study will be helpful for the meteorology department of various nations in detecting and predicting real-time weather using remote sensing data.</p>

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Meteorology-Net: A Deep Learning Framework for Multi-class Weather Phenomenon Detection, Classification and Forecasting Using Remote Sensing Data

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Yogesh H. Bhosale

摘要

The recent Artificial Intelligence (AI) boom has reignited interest in utilizing effective Deep Learning (DL) techniques in various domains. There exists proof to suggest that by integrating Machine Learning (ML) based data analysis with prediction and Convolutional Neural Networks (CNN’s) within the weather detection, forecasting pipeline, and weather recommendations can be improved. This paper tried to examine whether DL techniques can complement traditional numerical weather simulations and data aggregation platforms by enhancing image-based classification and forecasting accuracy. The proposed Meteorology-Net utilizes the DL-based 4-CNNs variants (VGG19, MobileNet, ResNet152V2, InceptionV3) for feature extraction using weather images. The dataset comprised 6862 weather images across 11 classes (dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow). Augmentation techniques such as rotation, scaling, and flipping were applied to balance class distribution (~ 1050 - 1100 samples per class) for weather prediction. Based on the attained results the base DCNNs reached the accepted range of accuracy. Hence to improve the performance of 11-class weather prediction the proposed Meteorology-Net integrates decision-level ensemble concatenation with a multi-task learning framework, enabling simultaneous classification of weather phenomena and forecasting of weather cues. Our proposed Meteorology-Net decision-level ensemble outperformed base DCNNs by achieving higher accuracy (0.9798 vs. 0.9773), improved precision and recall, and reduced error rates, demonstrating superior robustness and generalization for multi-class weather classification. We hope that this study will be helpful for the meteorology department of various nations in detecting and predicting real-time weather using remote sensing data.