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The aim is to review advanced deep learning techniques for predicting air pollution, assessing their effectiveness and emerging trends.

A Comprehensive Review on Advanced Deep Computational Techniques for Air Pollution Prediction

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The aim is to review advanced deep learning techniques for predicting air pollution, assessing their effectiveness and emerging trends.
Systematic literature review following PRISMA 2020 guidelines
Evaluation of deep learning architectures and environmental data sources
Comparative analysis of model performance and metrics such as RMSE, MAE, and R².
Transformer-based and hybrid models show superior predictive performance compared to traditional techniques.
The review identifies data scarcity and model interpretability as key challenges.
Emerging trends include Explainable Artificial Intelligence and Edge AI for air quality predictions.

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Comprehensive review evaluates deep learning methods for air pollution prediction, highlighting implications for air quality management.

Yeboah et al. (2026) studied this question.

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