IoT Inspired Monitoring System for Air Pollution using Random Forest Classifier

Air pollution is affecting millions of people globally and is becoming a major health concern. Furthermore, it has harmful gasses and (PM2.5), or tiny microscopic particulate matter, which lower the air quality. Not only has this been the primary focus of scientific investigation, but it has also become a major social issue in the lives of the general population. This assertion is supported by the World Health Organization (WHO) estimate that the health effects due to air pollution result in 2.4 million fatalities each year. Chronic exposure to air pollution can lead to a range of serious health problems, including respiratory and lung conditions. Numerous organizations alert people to the fact that severe air pollution exists in many parts of the world. Governments and central agencies may find that these models have policy implications for preventing excessive pollution levels. Although there have been several attempts to simulate pollution levels in the literature, better data integration and more accurate prediction results are anticipated due to current developments in machine learning and deep learning approaches. This paper presents a thorough investigation into the modeling of actual air pollution data using machine learning frameworks. In this study five basic machine learning algorithms have been compared to predict the air quality. The Random Forest algorithm gives the best accuracy of 98%.