室内空气可培养细菌浓度的快速估算模型：机器学习的应用

Liu Zhijian, Li Hao, Cao Guoqing

Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China.

Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA.

Int J Environ Res Public Health. 2017 Jul 30;14(8):857. doi: 10.3390/ijerph14080857.

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 and PM), 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.

室内空气中可培养细菌有时对人体健康有害。因此，快速估计它们的浓度尤为必要。然而，测量室内微生物浓度（如细菌）通常需要大量时间、经济成本和人力。在本文中，我们旨在提供一种快速解决方案：利用基于知识的机器学习，仅通过几个可测量的室内环境指标（包括室内颗粒物（PM和PM）、温度、相对湿度和二氧化碳浓度）的输入，快速估计室内空气中可培养细菌的浓度。我们的结果表明，基于使用包含249个数据组的实验数据库进行的模型训练和测试，广义回归神经网络（GRNN）模型能够充分提供快速且合适的估计。