Estimating Pavement Temperature Using Machine Learning Models Based on Climatic Data

TERZİ Ö., Baykal T., KARAHANÇER Ş., ERİŞKİN E., TERZİ S.

International Airfield and Highway Pavements Conference 2025: Design, Construction, Condition Evaluation, and Management of Pavements, Arizona, Amerika Birleşik Devletleri, 8 - 11 Haziran 2025, ss.391-400, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1061/9780784486214.036
  • Basıldığı Şehir: Arizona
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.391-400
  • Anahtar Kelimeler: Cat Boost, LightGBM, machine learning, pavement temperature
  • Isparta Uygulamalı Bilimler Üniversitesi Adresli: Evet

Özet

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 R2 and RMSE metrics, with results indicating that R2 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.