Forecasting Air Quality Index (AQI) Using Machine Learning Models: A Comparative Study

Abstract

One of the biggest environmental issues affecting our health and well being is air quality, an unseen danger that we breathe every day. Packed with dangerous gases and microscopic particles, poor air quality is a silent killer that can cause anything from chronic respiratory issues to serious, life-threatening diseases. The enormity of this issue emphasises how urgently we need information that is easy to understand in order to safeguard our communities. By concentrating on the Air Quality Index (AQI), a simple method of expressing how clean or polluted the air is, this research directly addresses that challenge. The goal is to uncover the hidden narrative within the numbers by employing intelligent computer models (machine learning) to sort through years’ worth of air pollution data, includingdaily readings of smog, soot, and other pollutants. The objective is to increase the usefulness of the AQI by creating a system that can precisely forecast the air quality for tomorrow, informing us of two important factors: the likelihood that the air will be unhealthy (the probability of it reaching a critical level) and how bad it will likely be(the predicted AQI number).