DOI: 10.3390/ijerph14080857. Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning

10.3390/ijerph14080857

Crossref journal-article

MDPI AG

International Journal of Environmental Research and Public Health (1968)

Abstract

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.

Bibliography

Liu, Z., Li, H., & Cao, G. (2017). Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning. International Journal of Environmental Research and Public Health, 14(8), 857.

Authors 3

Zhijian Liu (first)
Hao Li (additional)
Guoqing Cao (additional)

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Dates

Type	When
Created	8 years ago (Aug. 1, 2017, 3:30 a.m.)
Deposited	1 year, 2 months ago (June 8, 2024, 1:30 a.m.)
Indexed	3 days, 15 hours ago (Aug. 27, 2025, 11:34 a.m.)
Issued	8 years, 1 month ago (July 30, 2017)
Published	8 years, 1 month ago (July 30, 2017)
Published Online	8 years, 1 month ago (July 30, 2017)

Funders 0

None

BibTeX

@article{Liu_2017, title={Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning}, volume={14}, ISSN={1660-4601}, url={http://dx.doi.org/10.3390/ijerph14080857}, DOI={10.3390/ijerph14080857}, number={8}, journal={International Journal of Environmental Research and Public Health}, publisher={MDPI AG}, author={Liu, Zhijian and Li, Hao and Cao, Guoqing}, year={2017}, month=jul, pages={857} }

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