Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm

Authors

  • Xiaojie Zhou
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia; Weifang University of Science and Technology, Shouguang 262713, China
  • Majid Khan Majahar Ali
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
  • Farah Aini Abdullah
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
  • Lili Wu
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
  • Ying Tian
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
  • Tao Li
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
  • Kaihui Li
    School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia

Keywords:

Air quality prediction, DBO, CNN, LSTM, Attention

Abstract

Air pollution significantly impacts human health and socioeconomic development, making accurate air quality prediction crucial. This study proposes a hybrid CNN-LSTM-Attention model optimized with an improved Dung Beetle Optimization (IDBO) algorithm to enhance predictive performance. IDBO integrates multiple strategies to improve global search capabilities and overcome the limitations of conventional DBO. Experiments using PM2.5 data from Penang, Malaysia, demonstrate that the proposed model outperforms other models across multiple evaluation metrics R2 = 0.904, RMSE = 2.677, MSE = 7.168, MAE = 1.982, MAPE = 44.1% The findings validate the effectiveness of the proposed approach in improving air quality prediction, offering valuable insights for environmental monitoring and pollution control.

Dimensions

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Published

2025-08-01

How to Cite

Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2473. https://doi.org/10.46481/jnsps.2025.2473

Issue

Volume 7, Issue 3, August 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 xiaojie zhou, Majid Khan Majahar Ali, Farah Aini Abdullah, Lili Wu, Ying Tian, Tao Li, Kaihui Li

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2473. https://doi.org/10.46481/jnsps.2025.2473

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