Sustainable Cities and Society (Gold Open Access), 101, 105140
Health|구충완
A machine learning-based forecasting model for personal maximum allowable exposure time under extremely hot environments
2024International Journals
Research AreaHealth
Professor구충완
AuthorsChoi, Y., Seo, S., Lee, J., Kim, T.W., and Koo, C. (Corresponding Author)
Publication Year2024
Journal (Volume, Issue, Pages)Sustainable Cities and Society (Gold Open Access), 101, 105140
AbstractAs global warming leads to an increase in the frequency and intensity of heatwaves, protecting outdoor workers from heat-related illnesses becomes paramount. To address this challenge, this study aimed to forecast personal maximum allowable exposure time by considering individual differences under extremely hot environments. To enhance the prediction accuracy of the proposed approach, a machine learning-based error correction model was developed in conjunction with the PHS_HR method (i.e., predicted heat strain index using real-time heart rate), reflecting dynamic changes in personal biometric characteristics (e.g., heart rate, body fat percentage, etc.) as well as environmental conditions and exposure time. Among the developed models, the multi-layer perceptron (MLP) algorithm demonstrated a high degree of reliability in forecasting personal maximum allowable exposure time, achieving a mean absolute error (MAE) of 0.19 minutes (11 seconds), compared to the existing PHS_HR method (MAE: 5.05 minutes). This study highlights the potential benefits of data-driven computational methods in occupational health and safety management. By providing more accurate predictions of personal maximum allowable exposure time under extremely hot environments, the risk of heat-related illnesses outdoors can be effectively mitigated. As heatwaves become more prevalent, the proposed approach offers a more valuable tool to ensure safer work environments.
Extremely Hot EnvironmentConstruction WorkersContinuous Work TimePredicted Heat StrainMachine Learning Algorithm