Air Quality Prediction and Monitoring using Machine Learning Algorithm based IoT sensor- A researcher's perspective

Air Pollution (AP) is one of the serious and major environmental problem worldwide. Many researchers have drawn attention and have focused about these problems keeping in mind human health. Air quality prediction information is one of the better ways through which people can be informed to be more vigilant about serious health issues and protect human health caused by air pollution. In many metropolitan cities air pollution is a major challenging environmental issue. To analyze the present traffic condition of the city, local authorities can be enabled by real time monitoring of pollution data which makes appropriate decisions. Hence an early system is required for monitoring and calculating the level of AP using Air Quality (AQ) which is essential for predicting exactly the pollutant concentrations. The prediction of AQ can be improved by deploying Internet of Things (IoT) based sensor which are considerably changing the prediction of AQ dynamically. The prediction of AP discussed and estimated using many existing techniques are very expensive and have very low accuracy. The technological advancement of Machine Learning (ML) algorithm can be very fast increasing and searching almost all fields and applications whereas AP prediction has not prohibited from those fields. This paper describes about various studies of ML algorithm relating to AP prediction and monitoring based on the IoT sensor data in the context of various cities. This paper also summaries real time and historical data based on the AQ prediction models tools and techniques and describes about recent research methodologies merits and demerits of AQ prediction, along with the challenges based on real time monitoring and prediction of AQ.