Air Quality Prediction System using ML and DL Techniques

In this modern world, air pollution is one of the emerging global issues, and it is caused due to release of harmful gases released by various factories, vehicles etc. It creates disturbance to the entire ecosystem. So, it is very much essential to control this. In order to control air pollution along with measurement, prediction also plays a major role. With the advent of new technologies human life became more comfortable, IoT is one among them. Sensors are the main component for any IoT device and with the use of different sensors, detection of pollution in air has become very easy. But it is difficult to detect pollution content continuously at faraway places, in this case there is a need for prediction system that can helps us. The main idea of our proposed system is to build a machine learning model by using previously collected data by various sensors in a particular area. In order to build this prediction system, we have used various ML and DL algorithms. This paper employs PM2.5 and PM10 as significant parameters which forms basis for the prediction of quality of air. And it considers RMSE for comparison of different models to finalize one best model which will predict air quality with high accuracy.