Evaluation of air temperature with machine learning regression methods using Seoul City meteorological data

Weather has a significant impact on human life and activities. As abrupt changes in air temperature negatively affect daily life and various industries, the importance of weather forecast accuracy is increasing day by day. Current weather forecasting methods can be divided into two main groups: numerical-based and machine learning-based approaches. Numerical-based weather forecasting methods use complex mathematical formulas that significantly increase the computational cost. On the other hand, machine learning-based methods have been preferred more in recent years due to their lower computational costs. In this study, the next day's maximum and minimum air temperature are estimated for Seoul, South Korea by using 12 different regression methods together with the boosting-based machine learning algorithms developed in recent years, as well as traditional machine learning methods. Furthermore, since tuning of hyperparameters affects the process time and performance of machine learning algorithms, all 12 methods have been extensively studied in terms of time and hyperparameters. The square correlation coefficient (R-2), which is frequently adopted in the literature, is used to compare the performances of the methods. According to the observed results, the boosting-based XGBoost and LightGBM methods are the most successful machine learning algorithms in predicting the maximum and minimum air temperature for all years with both statistical test analysis and the highest R-2 score.