Prediction of Hazardous Weather Phenomena Using Artificial Intelligence

The prediction of hazardous weather phenomena is a critical component in ensuring the safety and efficiency of air transport operations. This paper focuses on evaluating the potential of artificial intelligence (AI) in forecasting fog—one of the most significant weather conditions affecting airport visibility. The methodological framework combines empirical methods (observation, measurement, experimentation) with theoretical approaches (analysis, synthesis, modeling). Emphasis is placed on the application of machine learning and deep learning techniques for processing meteorological data collected from Sliac military airport. The study compares conventional numerical weather prediction models (e.g., WRF) with AI-based approaches such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) neural networks, and ensemble models. Results indicate that AI models achieve higher accuracy in short-term fog prediction while reducing computational requirements. Experiments demonstrated success rates of up to 90% using ensemble techniques. The findings confirm that AI represents a promising tool for developing modern predictive meteorological systems in aviation. Challenges identified include limited data availability, the need for high-quality datasets, and the complexity of model interpretation. Future work should include expanding the data scope to multiple airports and incorporating satellite and radar data. The proposed approach offers a strong foundation for the advancement of intelligent, automated decision-support systems in both civil and military aviation meteorology.