Climate-induced thermoregulatory responses in a non-linear thermal environment: investigating the inter-dependencies using a facile artificial neural network-based predictive strategy

Objectives. Given the burgeoning impacts of climatic variability on human health, suitable computational paradigms are used to explore the subsequent ergonomic repercussions. The artificial neural network (ANN), in particular, exhibits near-accurate input-output mapping. However, employment of the ANN to trace the inter-dependencies between the climatic and human thermoregulatory parameters in real-world fuzzy problem landscapes is relatively inadequate. In the present study, the ANN models examined the relationships between climatic, behavioral and intrinsic input factors and the thermoregulatory outputs, namely, sweating and the evaporative heat transfer at the skin surface (E-sk). Methods. The data were obtained from nearly 1800 subjects who were exposed to a hot and humid climate outdoors. The ANN models were trained using the Levenberg-Marquardt algorithm combined with Bayesian regularization. Results. The predictability of the ANN models was statistically substantiated. The clothing insulation factor was not included as an input parameter, given its similar values. Intriguingly, the ANN results indicated that fabrics with similar thermal resistances could still affect E-sk, plausibly owing to the temporal variation in the evaporative resistance of fabrics among individuals. Conclusion. The reasonably accurate results affirmed the suitability of ANN as a pragmatic technique that could elucidate heat-induced ergonomic challenges.

Automated summary

By analyzing the impacts of climatic, behavioral, and intrinsic factors on thermoregulation using artificial neural network (ANN) models, it was found that ANN can accurately predict sweating and evaporative heat transfer at the skin surface. This affirms the suitability of ANN as a pragmatic technique for addressing heat-induced ergonomic challenges.