Artificial Intelligence and Machine Learning for Air Pollution Monitoring and Prediction

Author

Kotipalli. Harshitha, Gudla. Pramodh, Lanka. Vasu, Chukka. Mohan Krishna


Abstract

 The consequences of air pollution caused by speedy industrialization and urbanization are indeed a serious issue for human health and environmental sustainability. Older monitoring methods are based on costly and widely-spaced physical monitoring stations with no predictive capabilities. Current approaches involve the use of Internet of Things (IoT) sensor networks and sophisticated machine learning (ML) and deep learning (DL) models to overcome these challenges. This review combines the results of several fundamental works related to environmental sensing through the Internet of Things, supervised learning methods (Random Forest, XGBoost), deep temporal learning methods (Long Short-Term Memory networks, CNN-LSTM networks), and specific industrial monitoring applications. The published materials demonstrate that AI-based architectures can significantly improve predictive accuracy, real-time data imputation, and proactively manage cases of pollution. However, more technological development is needed to address issues of sensor calibration drift, crossregional generalizability, and data class imbalances.



Keywords

Air quality monitoring, Machine learning, Deep learning, Internet of Things (IoT), Spatio-temporal correlation, Sensor calibration



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References


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