A Machine Learning Model for Air Quality Prediction for Smart Cities

Air quality of a certain region can be used as one of the major factor determining pollution index also how well the city’s industries and population is managed. Urban air quality monitoring has been a constant challenge with the advent of industrialization. Air pollution has remained a major challenge for the public and the government all over the world. Air pollution causes noticeable damage to the environment as well as to human health resulting into acid rain, global warming, heart diseases and skin cancer to the people. This paper addresses the challenge of predicting the Air Quality Index (AQI), with the aim to minimize the pollution before it gets adverse, using two Machine Learning Algorithms: Neural Networks and Support Vector Machines. The air pollution databases were extracted from the Central Pollution Control Board (CPCB), Ministry of Environment, Forest and Climate change, Government of India. The proposed Machine Learning (ML) model is promising in prediction context for the Delhi AQI. The results show improvement of the prediction accuracy and suggest that the model can be used in other smart cities as well.