Systematic Literature Review on Deep Learning for Weather Phenomenon Classification

Nowadays, weather image classification is very important, it can help many sectors, such as daily weather forecasting, disaster control, agriculture, aviation, etc. Deep learning has emerged as an effective method for image classification, providing significant increases in efficiency and accuracy. This study presents a thorough examination of the application of deep learning in weather image classification, with an emphasis on methodology, challenges, and future works. The PRISMA approach was used to systematically search and filter 25377 publications (2018–2024) from IEEE, Elsevier, Springer, Wiley Library, and MDPI. The result was a selection of 16 important studies that showcase cutting-edge methodologies and the effectiveness of those measures. Our goal is to present a comprehensive overview of how deep learning methods have improved weather prediction while identifying potential topics for future research.