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

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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.
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Keywords: Indoor airborne culturable fungi; PM10; PM2.5; kernel-based extreme learning machine (K-ELM); real-time prediction

Document Type: Research Article

Affiliations: 1: Department of Power Engineering, North China Electric Power University, Baoding, Hebei, PR China 2: School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang, China 3: Department of Civil Engineering, Technical University of Denmark, Kongens Lyngby, Denmark 4: Institute of Building Environment and Energy, China Academy of Building Research, Beijing, PR China

Publication date: May 3, 2020

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