Machine Learning-Based Real-Time Air Pollution Monitoring and Forecasting Using IoT and Environmental Sensor Data: A Survey

The surveyed studies show that machine learning and deep learning can forecast air quality and the AQI with useful accuracy, and that IoT sensors can supply the live data such models need. They also show that ensemble and hybrid methods often perform well, and that interpretability and real-time usability remain important. Building on these findings, this paper described an Arduino-based monitoring unit combined with historical data, a comparison of Random Forest, Decision Tree, SVM and ANN models, and a Java web-based application for real-time display and early warnings. The system aims to move air quality monitoring from reporting present conditions toward forecasting future ones.

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