期刊
出版社
MDPI AG
DOI: 10.3390/ijerph14080857
关键词
indoor airborne culturable bacteria; PM2.5 and PM10; estimation model; machine learning; artificial neural network
资金
- key research projects planned in the 13th Five-Year: Green building and building industrialization [2017YFC0702804]
- Natural Science Foundation of Hebei [E2017502051]
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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