Abstract
Monitoring air pollution is of increasing concern today. People are suffering from health problems as a result of prolonged exposure to polluted environments. This project aims to develop an air quality monitoring system using machine learning with Internet of Things (IoT), an Internet server network of physical nodes. This system consists of three sections: an air pollution detection model developed in python and built using machine learning algorithms, random forest and support vector machine, a low-cost air monitoring device comprising of a hardware unit that detects different pollutants like CO, NOx, PM2.5, and an IoT Cloud, ThingSpeak, acting as a middleman for the captured data between the hardware component and the algorithm for air pollution classification. The final output displays the predicted air quality index (AQI) and provides a comparison between the two algorithms used, random forest and support vector machine, in terms of accuracy and various other statistical data. The accuracy depicted by random forest exceeds 95% and that of support vector machine is 85%.
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Varshitha Chandra, B.R., Nair, P.G., Khan, R.I., Mahalakshmi, B.S. (2021). Air Quality Monitoring System Using Machine Learning and IoT. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1334. Springer, Singapore. https://doi.org/10.1007/978-981-33-6981-8_54
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DOI: https://doi.org/10.1007/978-981-33-6981-8_54
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