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Pollutant recognition based on supervised machine learning for Indoor Air Quality monitoring systems

Saad, S. M. and Andrew, A. M. and Shakaff, A. Y. M. and Dzahir, M. A. M. and Hussein, M. and Mohamad, M. and Ahmad, Z. A. (2017) Pollutant recognition based on supervised machine learning for Indoor Air Quality monitoring systems. Applied Sciences (Switzerland), 7 (8). ISSN 2076-3417

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as 'unknown', because the system cannot recognize the input of the new pattern.

Item Type:Article
Uncontrolled Keywords:Indoor air quality, Pollutants recognition, Supervised machine learning
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:75343
Deposited By: Widya Wahid
Deposited On:22 Mar 2018 11:03
Last Modified:22 Mar 2018 11:03

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