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

Zhijian Liu*, Shengyuan Ma, Lifeng Wu, Hang Yin, Guoqing Cao

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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 PM10 and PM2.5, RH, Temperature, CO2 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 PM10 and RH provides a more suitable selection for predicting the ICF.
Original languageEnglish
JournalInternational Journal of Environmental Health Research
Volume30
Issue number3
Pages (from-to)344-356
ISSN0960-3123
DOIs
Publication statusPublished - 2020

Keywords

  • Indoor airborne culturable fungi
  • PM2.5
  • PM10
  • Real-time prediction
  • Kernel-based extreme learning machine (K-ELM)

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