Understanding the Interrelation Between Temperature and Meteorological Factors: A Case Study of Szeged Using Machine Learning Techniques

Temperature serves as a fundamental indicator of thermal conditions, influencing various natural processes and human activities. This study investigates the relationship between temperature and other meteorological factors, including humidity, wind speed, visibility, pressure, and apparent temperature, using historical weather data from Szeged, Hungary (2006-2016). Employing multiple regression models and advanced machine learning algorithms such as XGBoost and Artificial Neural Networks (ANNs), the research aims to elucidate the linear and non-linear dependencies of temperature on these factors. The findings indicate a significant linear correlation, with XGBoost outperforming traditional regression approaches in predicting temperature variations. This study contributes to enhancing temperature forecasting accuracy, which is crucial for improving quality of life and informing climate-related decision-making processes.