An ensembled method for air quality monitoring and control using machine learning

Abstract

Air quality monitoring is a significant job in our everyday life and affects the vitality of a human. It paves a better way of understanding the sources, effects and levels of pollutants in the air inhaled by the population of the region. This concept helps us in improving and developing pollution control measures to diminish the effect of air contamination. Air Quality Index (AQI) helps in understanding the level of pollution. This study briefs various state of art methods such as SVM, RF,ANN, RNN and FL in predicting air quality using machine learning. The results shows prominent outcomes which can be deployed in various cost effective hardware platforms for household and commercial purposes. The proposed model used eight parameters like NO2CO, O3, PM2.5, PM10, SO2, TEMP, PRES, DEWP, RAIN,WD, WSPM of Beijing dataset in order to predict AQI and it is tested with regional dataset. The Proposed model works based on process monitoring model. The efficient and flexible way to solve environmental issues is to combine machine learning algorithm with air quality prediction.