Predicting the concentration of indoor culturable fungi using a kernel-based extreme learning machine (K-ELM).

Journal: International journal of environmental health research
Published Date: Jun 1, 2020

Abstract

Indoor fungal is of great significance for human health. The kernel-based extreme learning machine is employed to determine the most important parameters for predicting the concentration of indoor culturable fungi (ICF). For model training and statistical analysis, parameters that contained indoor or outdoor PM and PM, RH, Temperature, CO and ICF were measured in 85 residential buildings of Baoding, China, from November 2016 to March 2017. The variable selection process contains four different cases to identify the optimal input combination. The results indicate that root mean square error of the optimal input combinations can be improved 5.6% from 1 to 2 input variables, while that could be only improved 1.9% from 2 to 3 input variables. However, considering both precision and simplicity, the combination of indoor PM and RH provides a more suitable selection for predicting the ICF.

Authors

  • Zhijian Liu
    Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
  • Shengyuan Ma
    Department of Power Engineering, North China Electric Power University, Baoding, Hebei, PR China.
  • Lifeng Wu
    School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang, China.
  • Hang Yin
    Department of Gastroenterology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200011, China.
  • Guoqing Cao
    Institute of Building Environment and Energy, China Academy of Building Research, Beijing, PR China.

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