Smar Pollution Tracking: Leveraging AI For Real-Time Environmental Risk Management
DOI:
https://doi.org/10.64252/hrtkf049Keywords:
AI in environmental monitoring, pollution tracking, real-time risk management, machine learning, smart city, environmental sensors, air quality prediction, water pollution detection, anomaly detection, environmental sustainability.Abstract
Environmental pollution is a severe menace to the health of people and ecology, which requires smart and active measures. The idea of developing a clever pollution tracking system that incorporates Artificial Intelligence (AI) to manage environmental risks in real-time is discussed in the present paper. The suggested system consists of sensor networks, machine learning, and cloud-based analytics that will allow monitoring, predicting, and responding to air and water pollution levels in real time. Simulated urban deployments have shown the model to be very efficient in both prediction accuracy and system responsiveness, indicating potential at scale urban and industrial application. The ultimate goal of this work is to reduce the gap between environmental monitoring and automated decision-making that will have a positive effect on sustainability and people health.
