Machine Learning-Based Forecasting of Air Quality Index Using Data Analytics

Abstract

Air pollution is a growing environmental issue that significantly affects human health and ecological balance. The Air Quality Index (AQI) is commonly used to indicate pollution levels, but most existing systems only provide current or past information without predicting future conditions. To address this limitation, this study presents a machine learning-based approach for forecasting AQI using historical pollutant data and environmental parameters. Data from various sources is processed and analyzed to identify patterns that influence air quality. The proposed system applies machine learning models to predict future AQI values and analyze pollution trends over time. It also includes visualization techniques to present results in a clear and understandable manner. By providing early predictions and meaningful insights, the system helps individuals and authorities take timely actions to reduce health risks and manage air pollution effectively. The system is designed to be scalable and adaptable across different regions using diverse data sources. It ensures efficient performance with minimal computational overhead, making it suitable for real-time applications. Overall, the approach enhances proactive environmental monitoring and supports sustainable decision-making.