Pre-Trained Convolutional Neural Network Benchmark For Multi-Class Weather Modeling - Ramadhany, Sinta Dhea; Yohannes, Ervin (Journal of Informatics and Computer Science (JINACS), 2025)
Abstract— Weather forecasting plays a crucial role in reducing the risks of extreme events that threaten human safety, economic stability, and the environment. Traditional forecasting methods relying on manual observation have developed into modern approaches using satellite, radar, and computational models; however, prediction accuracy remains limited due to the complexity of atmospheric systems and data constraints. In this context, deep learning, particularly Convolutional Neural Networks (CNNs), provides significant potential for automatic weather classification through digital imagery. This study evaluates and compares the performance of four pre-trained CNN architectures VGG16, ResNet50, AlexNet, and InceptionV3 on the Kaggle “Multi-class Weather Dataset,” which contains 860 images categorized into four classes: Cloudy, Shine, Rain, and Sunrise. The methodology involves data augmentation, fine-tuning, and systematic experimentation with various hyperparameters and data split ratios to enhance model generalization. The evaluation metrics applied include accuracy, precision, recall, and F1-score. Experimental results reveal that InceptionV3 outperforms other models, achieving up to 98% training accuracy and 96% validation accuracy due to its effective multi-scale feature extraction and regularization. ResNet50 delivers balanced results with validation accuracy up to 94%, while AlexNet records relatively high detection counts but lower overall performance. In contrast, VGG16 yields the lowest accuracy among the tested models. These findings highlight InceptionV3 as the most robust architecture for weather image classification and emphasize the importance of model selection in balancing prediction accuracy and computational efficiency. The study contributes as a foundation for the development of deep learning-based weather recognition systems that can support early warning applications and disaster risk reduction. Keywords— Convolutional Neural Network, Weather Classification, ResNet50, VGG16, AlexNet, InceptionV3