Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm
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 integrat...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nigerian Society of Physical Sciences
2025-08-01
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| Series: | Journal of Nigerian Society of Physical Sciences |
| Subjects: | |
| Online Access: | https://journal.nsps.org.ng/index.php/jnsps/article/view/2473 |
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| _version_ | 1849431386097713152 |
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| author | Xiaojie Zhou Majid Khan Majahar Ali Farah Aini Abdullah Lili Wu Ying Tian Tao Li Kaihui Li |
| author_facet | Xiaojie Zhou Majid Khan Majahar Ali Farah Aini Abdullah Lili Wu Ying Tian Tao Li Kaihui Li |
| author_sort | Xiaojie Zhou |
| collection | DOAJ |
| description |
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.
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| format | Article |
| id | doaj-art-917de63df0444ac584ca2ac80e076597 |
| institution | Kabale University |
| issn | 2714-2817 2714-4704 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | Journal of Nigerian Society of Physical Sciences |
| spelling | doaj-art-917de63df0444ac584ca2ac80e0765972025-08-20T03:27:40ZengNigerian Society of Physical SciencesJournal of Nigerian Society of Physical Sciences2714-28172714-47042025-08-017310.46481/jnsps.2025.2473Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithmXiaojie ZhouMajid Khan Majahar AliFarah Aini AbdullahLili WuYing TianTao LiKaihui Li 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. https://journal.nsps.org.ng/index.php/jnsps/article/view/2473Air quality prediction DBOCNNLSTMAttention |
| spellingShingle | Xiaojie Zhou Majid Khan Majahar Ali Farah Aini Abdullah Lili Wu Ying Tian Tao Li Kaihui Li Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm Journal of Nigerian Society of Physical Sciences Air quality prediction DBO CNN LSTM Attention |
| title | Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm |
| title_full | Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm |
| title_fullStr | Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm |
| title_full_unstemmed | Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm |
| title_short | Air quality prediction enhanced by a CNN-LSTM-Attention model optimized with an advanced dung beetle algorithm |
| title_sort | air quality prediction enhanced by a cnn lstm attention model optimized with an advanced dung beetle algorithm |
| topic | Air quality prediction DBO CNN LSTM Attention |
| url | https://journal.nsps.org.ng/index.php/jnsps/article/view/2473 |
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