PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model
The concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 predictio...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9399408/ |
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| author | Minh Hieu Nguyen Phi Le Nguyen Kien Nguyen Van An Le Thanh-Hung Nguyen Yusheng Ji |
| author_facet | Minh Hieu Nguyen Phi Le Nguyen Kien Nguyen Van An Le Thanh-Hung Nguyen Yusheng Ji |
| author_sort | Minh Hieu Nguyen |
| collection | DOAJ |
| description | The concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 prediction, which can potentially contribute to reducing or avoiding the negative consequences. Our approach’s novelty is to utilize the genetic algorithm (GA) and an encoder-decoder (E-D) model for PM2.5 prediction. The GA benefits feature selection and remove outliers to enhance the prediction accuracy. The encoder-decoder model with long short-term memory (LSTM), which relaxes the restrictions between the input and output of the model, can be used to effectively predict the PM2.5 concentration. We evaluate the proposed model on air quality datasets from Hanoi and Taiwan. The evaluation results show that our model achieves excellent performance. By merely using the E-D model, we can obtain more accurate (up to 53.7%) predictions than those of previous works. Moreover, the GA in our model has the advantage of obtaining the optimal feature combination for predicting the PM2.5 concentration. By combining the GA-based feature selection algorithm and the E-D model, our proposed approach further improves the accuracy by at least 13.7%. |
| format | Article |
| id | doaj-art-717688386c1944f6a2f92efb46d85f1c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-717688386c1944f6a2f92efb46d85f1c2025-08-25T23:00:45ZengIEEEIEEE Access2169-35362021-01-019573385735010.1109/ACCESS.2021.30722809399408PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder ModelMinh Hieu Nguyen0https://orcid.org/0000-0001-6547-7641Phi Le Nguyen1Kien Nguyen2https://orcid.org/0000-0003-0400-3084Van An Le3Thanh-Hung Nguyen4Yusheng Ji5https://orcid.org/0000-0003-4364-8491School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, VietnamSchool of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, VietnamGraduate School of Engineering, Chiba University, Chiba, JapanDepartment of Informatics, The Graduate University for Advanced Studies, SOKENDAI, Tokyo, JapanSchool of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, VietnamDepartment of Informatics, The Graduate University for Advanced Studies, SOKENDAI, Tokyo, JapanThe concentration of fine particulate matter (PM2.5), which represents inhalable particles with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can penetrate deep into the human lungs and severely affect human health. This paper studies accurate PM2.5 prediction, which can potentially contribute to reducing or avoiding the negative consequences. Our approach’s novelty is to utilize the genetic algorithm (GA) and an encoder-decoder (E-D) model for PM2.5 prediction. The GA benefits feature selection and remove outliers to enhance the prediction accuracy. The encoder-decoder model with long short-term memory (LSTM), which relaxes the restrictions between the input and output of the model, can be used to effectively predict the PM2.5 concentration. We evaluate the proposed model on air quality datasets from Hanoi and Taiwan. The evaluation results show that our model achieves excellent performance. By merely using the E-D model, we can obtain more accurate (up to 53.7%) predictions than those of previous works. Moreover, the GA in our model has the advantage of obtaining the optimal feature combination for predicting the PM2.5 concentration. By combining the GA-based feature selection algorithm and the E-D model, our proposed approach further improves the accuracy by at least 13.7%.https://ieeexplore.ieee.org/document/9399408/PM 25genetic algorithmfeature selectionlong short-term memoryencoder-decoder model |
| spellingShingle | Minh Hieu Nguyen Phi Le Nguyen Kien Nguyen Van An Le Thanh-Hung Nguyen Yusheng Ji PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model IEEE Access PM 25 genetic algorithm feature selection long short-term memory encoder-decoder model |
| title | PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model |
| title_full | PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model |
| title_fullStr | PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model |
| title_full_unstemmed | PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model |
| title_short | PM2.5 Prediction Using Genetic Algorithm-Based Feature Selection and Encoder-Decoder Model |
| title_sort | pm2 5 prediction using genetic algorithm based feature selection and encoder decoder model |
| topic | PM 25 genetic algorithm feature selection long short-term memory encoder-decoder model |
| url | https://ieeexplore.ieee.org/document/9399408/ |
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