All Sciences Proceedings http://as-proceeding.com/ 1 st International Conference on Recent Academic Studies May 2-4, 2023 : Konya, Turkey https://as- proceeding.com/index.php/icras © 2023 Published by All Sciences Proceedings 92 Classification of Weather Phenomenon with a New Deep Learning Method Based on Transfer Learning Halit Çetiner * , Sedat Metlek 2 1 Vocational School of Technical Sciences, Isparta University of Applied Sciences, Türkiye 2 Vocational School of Technical Sciences, Burdur Mehmet Akif Ersoy University, Türkiye * (halitcetiner@isparta.edu.tr) Abstract – Recognition of weather conditions, which have an important effect on the planning of our daily lives, affects many events from transport to agriculture. Even on an ordinary day, the weather affects many events, from taking children to the market to taking a walk. In addition, in many commercial areas such as agriculture and animal husbandry, many issues from planting and planting time to production are directly or indirectly related to weather conditions. For these reasons, automatic analyses and classification of aerial images will provide significant convenience. New technologies based on deep learning are needed to minimize the errors of experts working in the towers established to monitor weather conditions. Deep learning based systems are preferred because they bring a new perspective to feature extraction and classification approaches in classical machine learning technologies. With deep learning based systems, it is possible to classify by obtaining distinctive features from different weather conditions. In this paper, a pre-trained architecture-based deep learning model is proposed to classify a dataset containing 6877 images of 11 weather conditions. In order to measure the effect of the proposed model on the performance, a comparison with the basic model is performed. The weather classification accuracy of the proposed model in the test set is 88%. This performance result shows that the model is competitive with its competitors. At this point, eleven different weather images can be automatically classified. As a result of the mentioned procedures, this study can be a reference for future weather classification studies. Keywords –Deep Learning, Weather, Transfer Learning, Resnet152v2, Classification I. INTRODUCTION People need to have information about the weather to plan their lives. For example, people plan their lives according to the weather in different events in daily life such as picking up their children from school, going to the market, going to the grocery store, going on holiday, walking, playing football, taking their children to the park. Planned living has an important effect on people's lives. In order to support this effect, it is necessary to have information about the weather. Many actions and events in which people are involved affect the weather. Many activities carried out during the day are carried out according to the weather. Observation stations developed to monitor weather events that vary from region to region are managed by experts and effective devices [1]. Artificial intelligence-supported decision support systems are needed to prevent human-based errors in an ordinary way. Weather events can be classified based on human observation, which is one of the classic classification methods. However, it is also possible to make mistakes in such classification due to fatigue or low attention during visual discrimination. For these reasons, there is a great need for high-precision, efficient systems that automatically classify weather images. In the literature, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), ResNet50, DenseNet, VGG16, InceptionV3 based deep