Back to Search
Start Over
Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning.
- Source :
-
International journal of environmental research and public health [Int J Environ Res Public Health] 2017 Jul 30; Vol. 14 (8). Date of Electronic Publication: 2017 Jul 30. - Publication Year :
- 2017
-
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 (PM <subscript>2.5</subscript> and PM <subscript>10</subscript> ), 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.<br />Competing Interests: The authors declare no conflict of interest.
Details
- Language :
- English
- ISSN :
- 1660-4601
- Volume :
- 14
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- International journal of environmental research and public health
- Publication Type :
- Academic Journal
- Accession number :
- 28758941
- Full Text :
- https://doi.org/10.3390/ijerph14080857