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A Multi-Level Hierarchical Framework for the Classification of Weather Conditions and Hazard Prediction

This paper presents a multilevel hierarchical framework for the classification of weather conditions and hazard prediction. In recent years, the importance of data has grown significantly, with various types like text, numbers, images, audio, and videos playing a key role. Among these, images make up a large portion of the data available. This application shows promise for various purposes, especially when combined with decision support systems for traffic management, afforestation, and weather forecasting. It's particularly useful in situations where traditional weather predictions are not very accurate, such as ensuring the safe operation of self driving cars in dangerous weather. While previous studies have looked at this topic with fewer categories, this paper focuses on eleven specific types of weather images. The goal is to create a model that can accurately predict weather conditions after being trained on a large dataset of images. Accuracy is crucial in real-life situations to prevent accidents, making it the top priority for this paper. This work lays the groundwork for future applications in weather prediction, especially in situations where human expertise is not available or may be biased. The framework, capable of classifying images into eleven weather categories: dew, frost, glaze, rime, snow, hail, rain, lightning, rainbow, and sandstorm, provides real-time weather information with an accuracy of 0.9329. The proposed framework addresses the growing need for accurate weather classification and hazard prediction, offering a robust solution for various applications in the field.

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