Estimating Pavement Temperature Using Machine Learning Models Based on Climatic Data

This study focuses on estimating pavement temperature using machine learning models based on various climatic parameters. Climatic parameters, including air temperature, precipitation, wind speed, relative humidity, specific humidity, surface pressure, dew point temperature, and wet-bulb temperature, are obtained from meteorological databases. These parameters directly affect pavement temperature and are used as input parameters for the machine learning models. Random Forests, Extra Tree, Gradient Boosting, XGBoosting, Light Gradient Boosting Machine, CatBoost, and Hist Gradient Boosting algorithms are employed to model this relationship. Model performance was evaluated using R (2) and RMSE metrics, with results indicating that R-2 values for the testing set ranged between 0.90 and 0.93, demonstrating the effectiveness of the applied algorithms. The study confirms the capability of machine learning algorithms to estimate pavement temperature effectively, highlighting LightGBM and CatBoost as particularly promising approaches. These findings offer valuable insights for future research on model selection and development in pavement temperature prediction.