Augmenting the Predictive Precision of Air Quality Index Estimation via Synergistic Integration of Machine Learning Paradigms and Optimization Heuristics

Abstract

Air pollution has become a serious issue that affects public health and the environment. To deal with this, it is important to predict the Air Quality Index (AQI) accurately. This research presents a method that combines machine learning techniques with optimization strategies to improve AQI prediction. The approach uses past environmental data and applies machine learning models, especially ensemble methods to understand the complex patterns between pollutants and air quality. In addition, optimization algorithms are used to fine-tune the models, helping them perform better and give more accurate results. Tests conducted on standard datasets show that this combined method gives higher accuracy and better performance compared to traditional approaches. Overall, this study highlights how machine learning and optimization can work together to solve real-world air quality prediction problems effectively.