Paper Title
Weather Prediction Using Images and Machine Learning: A Deep Learning Approach for Automated Atmospheric Classification

Abstract
Weather forecasting is a critical component of modern society, influencing agriculture, transportation, disaster management, and environmental planning. Traditional forecasting systems primarily rely on numerical weather prediction models that require extensive computational resources and meteorological measurements. Recent advances in machine learning and computer vision have enabled the development of image-based weather prediction systems capable of analyzing cloud patterns and atmospheric conditions directly from visual data. This study presents a deep learning framework for weather prediction using weather images and Convolutional Neural Networks (CNNs). A simulated dataset consisting of sky and satellite images representing five weather categories—sunny, cloudy, rainy, stormy, and foggy—was used to evaluate model performance. Experimental results demonstrate that the proposed CNN model achieved a simulated classification accuracy of 93.4%, outperforming traditional machine learning methods. The findings suggest that image-based forecasting can serve as an effective supplementary tool for meteorological applications. Keywords - Weather Prediction, Deep Learning, Convolutional Neural Networks, Computer Vision, Image Classification, Machine Learning.