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

Abstract

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.

Keywords:

• Thermoregulatory responses
• environmental ergonomics
• evaporative resistance
• artificial neural network
• Levenberg–Marquardt algorithm
• Bayesian regularization

Acknowledgements

The authors would like to earnestly thank the Honorable Vice Chancellor of Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI) for the permission granted and the cooperation accorded apropos successful accomplishment of the present study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data and research materials

Supplemental data for this article can be accessed at doi:10.1080/10803548.2019.1684640.

Additional information

Funding

This work was partly supported by the Department of Science & Technology (DST), Government of India [Grant Sanction No. DST/CCP/NHH/106/2017(G)].