Prediction of Atmospheric Pollution Using Hybrid Machine Learning Algorithms: A Review

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This review examines machine learning techniques for accurate air quality predictions, highlighting hybrid models and their implications for policy.

Hybrid machine learning algorithms improve predictive accuracy by integrating diverse data streams for atmospheric pollution.
The study reviews various machine learning techniques, emphasizing their role in forecasting air quality impacts on public health.
Future research should focus on developing adaptive hybrid models to address gaps in data availability and pollution dynamics.
Effective pollution forecasting can enhance environmental policies and strengthen air quality management strategies.

Zouglis et al. (2025) studied this question.

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View Full Paper

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Authors

This review examines machine learning techniques for accurate air quality predictions, highlighting hybrid models and their implications for policy.

Hybrid machine learning algorithms improve predictive accuracy by integrating diverse data streams for atmospheric pollution.
The study reviews various machine learning techniques, emphasizing their role in forecasting air quality impacts on public health.
Future research should focus on developing adaptive hybrid models to address gaps in data availability and pollution dynamics.
Effective pollution forecasting can enhance environmental policies and strengthen air quality management strategies.

Zouglis et al. (2025) studied this question.

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