AI-Based Air Quality Prediction and Pollution Source Analysis System for Delhi NCR
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Abstract
Delhi NCR is severely affected by pollution from Vehicles, industries, construction dust, and seasonal crop burning. Although data is available, it is dispersed across various systems and does not provide a clear picture of the primary cause of pollution or its potential effects. Decisions are often made only after the situation gets worse.
This paper describes an AI-based system for identifying pollution sources, predicting AQI levels, and supporting decision-making in Delhi NCR. The system gathers data from sensors, satellite sources, weather conditions and traffic pattern in a single platform.
It utilizes Machine Learning models to identify pollution hotspots, major sources, and predict future AQI levels. The results are presented using a real-time dashboard that helps authorities to monitor the condition and take action accordingly. The system is aligned with initiatives by the Ministry of Environment and related pollution control bodies in India.
This platform is built using Python for Machine Learning, React.js for frontend of dashboard and cloud services for data management and storage. It supports role-based access, where users can view and use the system according to their responsibilities where administrators monitor data, analysts study trends, and policymakers make decisions. The system supports environment sustainability goals and help in proper pollution management
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