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Int. J. Environ. Res. Public Health 2017, 14(8), 857; doi:10.3390/ijerph14080857

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

1,†
,
2,3,†,* and 4,*
1
Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
2
Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
3
Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
4
Institute of Building Environment and Energy, China Academy of Building Research, Beijing 100013, China
These authors contributed equally.
*
Authors to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 22 July 2017 / Accepted: 27 July 2017 / Published: 30 July 2017
(This article belongs to the Section Environmental Science and Engineering)
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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. View Full-Text
Keywords: indoor airborne culturable bacteria; PM2.5 and PM10; estimation model; machine learning; artificial neural network indoor airborne culturable bacteria; PM2.5 and PM10; estimation model; machine learning; artificial neural network
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Liu, Z.; Li, H.; Cao, G. Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning. Int. J. Environ. Res. Public Health 2017, 14, 857.

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