Smart AI-Based Air Pollution Monitoring and Prediction System

Abstract

Air pollution is a growing environment concern that directly impacts public health and the ecosystem. With rapid urbanization and the industrialization, monitoring air quality has become essential for sustainable living. The Air Quality Index (AQI) is a key indicator used to assess air pollution levels and communicate associated health risks to the public. The fundamental aim of the Smart AI-Based Air Pollution Monitoring and Prediction System is to monitor air quality and accurately predict the Air Quality Index (AQI) using Artificial Intelligence and Machine Learning techniques. The proposed system is developed using the Flask framework and employs Machine Learning algorithms such as Linear Regression, Decision Tree, and Random Forest. The system evaluates the performance of each algorithm using standard metrics like R2 Score, Mean Absolute Error (MAE), and Mean Squared Error (MSE) to identify the most accurate model for AQI prediction. The system categorizes AQI into levels ranging from Good to Hazardous and provides real-time health recommendations based on the prediction values. By offering an accessible and data-driven solution, this system aims to support individuals, policymakers, and environmental agencies in making informed decisions to reduce air pollution and improve public health.