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Appl. Sci. 2017, 7(8), 823; doi:10.3390/app7080823

Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

1
Faculty of Mechanical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
2
Center for Diploma Studies (CDS), Universiti Malaysia Perlis (UniMAP), Kampus UniCITI Alam, Chuchuh, Padang Besar 02100, Malaysia
3
Center of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Taman Muhibbah, Jejawi, Arau 02600, Malaysia
*
Author to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 29 July 2017 / Accepted: 8 August 2017 / Published: 11 August 2017
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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. View Full-Text
Keywords: indoor air quality; supervised machine learning; pollutants recognition indoor air quality; supervised machine learning; pollutants recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mad Saad, S.; Andrew, A.M.; Md Shakaff, A.Y.; Mat Dzahir, M.A.; Hussein, M.; Mohamad, M.; Ahmad, Z.A. Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems. Appl. Sci. 2017, 7, 823.

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