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

Abstract

Climate, and especially cities with hot climatic conditions, directly impact human life. In this study, hourly datasets from the central meteorological station in Diyarbak & imath;r 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 degrees C in 1990 in real terms, increased by 21.3% to 18.66 degrees C in 2022. According to the SARIMAX model predictions, Physiological Equivalent Temperature values in 2022 are estimated to rise to 21.42 degrees 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.