Modeling Universal Thermal Climate Index Thermal Stress in Iran’s Hot Zones Using Neural Networks and Naïve Bayes

Abstract Aim: This study aimed to assess the impact of heat stressors in various environments across the expansive hot regions of Iran, with the goal of supporting workforce health and well-being. Methods: This descriptive-analytical study was conducted in four main stages: (1) identifying and measuring factors influencing the Universal Thermal Climate Index (UTCI), (2) calculating the UTCI as the dependent variable, (3) applying neural network (NN) and Naïve Bayes (NB) algorithms to weigh and model the effective factors on the UTCI, and (4) evaluating the accuracy of each model. Data modeling utilized Python’s scikit-learn package (version 3.7) and the Orange toolkit. Results: The average UTCI values in hot-dry and hot-humid regions were 30.49 and 39.48, respectively. In models for hot-dry regions, both algorithms identified dry temperature (T a ) as a significant factor. For hot-humid regions, the NB algorithm identified mean radiant temperature (T mrt ) as the primary factor, while the NN algorithm highlighted dry temperature (T a ). Model accuracy ranged from 74% to 94%, with NN algorithms demonstrating higher accuracy compared to NB algorithms. Conclusion: In the models for hot-dry regions, both algorithms predicted T a and then T mrt as the principal factors. For hot-humid regions, it was inferred that T mrt is the main influencing factor on the UTCI.