IJERPH, Vol. 14, Pages 857: Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

IJERPH, Vol. 14, Pages 857: Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

International Journal of Environmental Research and Public Health doi: 10.3390/ijerph14080857

Authors: Zhijian Liu Hao Li Guoqing Cao

Indoor airborne culturable bacteria are sometimes harmful to human health. Therefore, a quick estimation of their concentration is particularly necessary. However, measuring the indoor microorganism concentration (e.g., bacteria) usually requires a large amount of time, economic cost, and manpower. In this paper, we aim to provide a quick solution: using knowledge-based machine learning to provide quick estimation of the concentration of indoor airborne culturable bacteria only with the inputs of several measurable indoor environmental indicators, including: indoor particulate matter (PM2.5 and PM10), temperature, relative humidity, and CO2 concentration. Our results show that a general regression neural network (GRNN) model can sufficiently provide a quick and decent estimation based on the model training and testing using an experimental database with 249 data groups.

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