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Background <p>Southeast Asia regularly experiences severe haze events driven by transboundary pollution, significantly impacting public health. Accurate short-term forecasting of particulate matter concentrations, especially PM<sub>10</sub>, is crucial for timely interventions.</p> Objective <p>To improve the prediction of hourly PM<sub>10</sub> pollution levels by integrating topological data analysis (TDA) with attention-based convolutional neural networks (ABCNNs), focusing on classifying air quality into eight severity levels.</p> Methods <p>The proposed framework combines CNNs, self-attention mechanisms, and persistent homology-derived topological features from three key environmental variables. PM<sub>10</sub> category labels were predicted 6, 12, and 24 hours ahead. Data from 15 stations in Malaysia (2019–2020) were used, with feature selection based on correlation analysis. Performance was benchmarked against standard models including Random Forest, Support Vector Classifier, and traditional ABCNNs.</p> Results <p>Topological ABCNNs outperformed all baseline models across all prediction horizons. For 6-hour predictions, the model achieved an average accuracy of 0.9677 and F1 score of 0.9770. For 12- and 24-hour predictions, average accuracies were 0.9512 and 0.9086, respectively. The model also maintained robust performance across regions and better predicted rare high-pollution events.</p> Conclusion <p>Incorporating topological features into ABCNNs significantly enhances predictive performance for air pollution classification. This hybrid model offers a scalable and accurate tool for environmental monitoring and public health planning, particularly in regions vulnerable to haze pollution.</p> Graphical abstract <p></p>

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Topological Attention-Based Convolution Neural Networks in Analyzing and Predicting Particulate Matter Pollution Level

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月11日

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  • Zixin Lin,
  • Nur Fariha Syaqina Zulkepli,
  • Mohd Shareduwan Mohd Kasihmuddin,
  • R. U. Gobithaasan

摘要

Background

Southeast Asia regularly experiences severe haze events driven by transboundary pollution, significantly impacting public health. Accurate short-term forecasting of particulate matter concentrations, especially PM10, is crucial for timely interventions.

Objective

To improve the prediction of hourly PM10 pollution levels by integrating topological data analysis (TDA) with attention-based convolutional neural networks (ABCNNs), focusing on classifying air quality into eight severity levels.

Methods

The proposed framework combines CNNs, self-attention mechanisms, and persistent homology-derived topological features from three key environmental variables. PM10 category labels were predicted 6, 12, and 24 hours ahead. Data from 15 stations in Malaysia (2019–2020) were used, with feature selection based on correlation analysis. Performance was benchmarked against standard models including Random Forest, Support Vector Classifier, and traditional ABCNNs.

Results

Topological ABCNNs outperformed all baseline models across all prediction horizons. For 6-hour predictions, the model achieved an average accuracy of 0.9677 and F1 score of 0.9770. For 12- and 24-hour predictions, average accuracies were 0.9512 and 0.9086, respectively. The model also maintained robust performance across regions and better predicted rare high-pollution events.

Conclusion

Incorporating topological features into ABCNNs significantly enhances predictive performance for air pollution classification. This hybrid model offers a scalable and accurate tool for environmental monitoring and public health planning, particularly in regions vulnerable to haze pollution.

Graphical abstract