AI-Driven Pollution Monitoring and Mitigation Framework for Delhi: Integrating Drones, IoT, and Predictive Analytics for Sustainable Air Quality Management
Keywords:
Air Pollution, Artificial Intelligence, IoT, Drones, Predictive Analytics, Smart Cities, Deep Learning, Meteorological, Environmental TechnologyAbstract
This paper presents an AI-driven framework for real-time monitoring, prediction, and mitigation of urban air pollution in Delhi, India. The proposed system integrates drone-based air quality sensing, IoT-enabled data collection, and AI predictive analytics to forecast pollution levels and recommend proactive interventions. By combining drone data, IoT sensors, and meteorological information, deep learning models forecast pollution spikes and optimize mitigation measures. The system offers a scalable, replicable model for proactive pollution management across global cities.
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P. Sharma and R. Singh, 'IoT and Machine Learning for Air Quality Prediction,' IEEE Trans. on Environmental Eng., 2023.
A. Kumar et al., 'AI-Enabled Urban Pollution Management Frameworks,' Int. Journal of Smart Cities, 2024
