Exploring the Potential of Deep Learning Techniques in Air Pollution Prediction

Abstract

Air pollution prediction is an essential aspect of modern environmental science, as it helps in anticipating pollution levels and taking necessary measures to reduce harmful effects on public health. Machine learning (ML) has proven to be a powerful tool for predicting air pollution levels, as it can identify patterns in large datasets and make accurate predictions. The air quality index (AQI) is a metric used to report air quality. It calculates the short-term effects of air pollution on an individual's health. Public education on the harmful health effects of local air pollution is the aim of the AQI. In Indian cities, the level of air pollution has dramatically increased. The air quality index can be calculated mathematically in a number of ways. One of the most intriguing methods for predicting and analyzing AQI is data mining. Finding the best technique for AQI prediction to support climate control is the goal of this paper. The best solution can be found by refining the most successful approach. As a result, this paper's work includes extensive research as well as the use of innovative methodologies.