Comparative Analysis of Weather Image Classification Using CNN Algorithm with InceptionV3, DenseNet169 and NASNetMobile Architecture Models
DOI:
https://doi.org/10.57152/predatecs.v2i2.1608Keywords:
Convolutional Neural Networks, DenseNet169, Inception V3, NASNetMobile, WeatherAbstract
Rapid weather changes have a significant impact on various aspects of human life, including social and economic development. Weather analysis traditionally relies on data from Doppler radar, weather satellites, and weather balloons. However, advancements in computer vision technology provide new opportunities to enhance weather prediction systems through image recognition and classification. Studies evaluating and comparing deep learning architectures for weather image classification remain limited.This research utilizes Convolutional Neural Networks (CNN) to classify weather images using three architectures: InceptionV3, DenseNet169, and NASNetMobile. The results show that InceptionV3 achieved 97.94% accuracy on training data, 92.34% on validation data, and 93.81% on test data. DenseNet169 achieved 98.09% accuracy on training data, 88.46% on validation data, and 92.33% on test data. NASNetMobile achieved 96.51% accuracy on training data, 87.82% on validation data, and 89.97% on test data. Based on these results, InceptionV3 is the optimal choice for weather classification due to its consistent performance.This research addresses the gap in evaluating CNN architectures for weather data and contributes to improving weather monitoring systems, early disaster warnings, and applications reliant on accurate predictions. These findings also provide a foundation for the development of advanced technologies in image analysis and weather forecasting in the future.
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