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Machine learning and statistical models for predicting indoor air quality
Author(s) -
Wei Wenjuan,
Ramalho Olivier,
Malingre Laeticia,
Sivanantham Sutharsini,
Little John C.,
Mandin Corinne
Publication year - 2019
Publication title -
indoor air
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.387
H-Index - 99
eISSN - 1600-0668
pISSN - 0905-6947
DOI - 10.1111/ina.12580
Subject(s) - indoor air quality , statistical model , air quality index , particulates , environmental science , linear regression , regression analysis , artificial neural network , machine learning , partial least squares regression , statistics , computer science , meteorology , environmental engineering , mathematics , geography , ecology , biology
Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario‐based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty‐seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.