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

Chang Che; Junchi Tian

doi:10.5281/zenodo.13924235

Authors

Chang Che The George Washington University
Junchi Tian The George Washington University

DOI:

https://doi.org/10.5281/zenodo.13924235

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n4a06

Disciplines:

Computer Science

Subjects:

Machine Learning

References:

38

Keywords:

Machine Learning, Artificial Neural Networks, Regression Model

Abstract

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.

Author Biographies

Chang Che, The George Washington University

The George Washington University, US.

Junchi Tian, The George Washington University

The George Washington University, US.

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