Forecasting Human Bioclimatic Comfort in a Hot–Dry Climate Using Sarimax Machine Learning: Diyarbakır, Turkey

Ahmet Koç, Murat Uçan, Sülem Şenyiğit Doğan, Mehmet Kaya, Gökhan Şahin, Erdal Akin

Climate, and especially cities with hot climatic conditions, directly impact human life. In this study, hourly datasets from the central meteorological station in Diyarbakır city center for the years 1990–2022 were utilized. These data were analyzed using RayMan Pro-2.1 software, and Physiological Equivalent Temperature values were derived. The obtained Physiological Equivalent Temperature values were analyzed using the SARIMAX model implemented on a machine learning infrastructure to uncover the changes between 2022 and 2050. According to the results obtained, the Physiological Equivalent Temperature value, which was 15.42 °C in 1990 in real terms, increased by 21.3% to 18.66 °C in 2022. According to the SARIMAX model predictions, Physiological Equivalent Temperature values in 2022 are estimated to rise to 21.42 °C by 2050, reflecting an increase of 14.79%. The aim of this study is to examine the temporal variations in human bioclimatic comfort values and provide a foundation for future predictions. This will contribute to the development of urban master plans by local and administrative authorities.