Prediction of Air Quality Index using genetic programming

Abstract

The Air Quality Index (AQI) is a tool used to measure the impact of air pollution on health over time. In the world, air pollution has significantly increased, and machine learning techniques are used to forecast and analyze AQI. We present a new way for using GP to evolve models for AQI forecasting in this work GP can evolve more accurate AQI forecasting models than other standard machine learning algorithms, according to experimental results using datasets obtained from various stations across multiple cities in India. Furthermore, while developing AQI forecasting models, GP can automatically identify significant features, and the models developed by GP are interpretable.