Classification prediction of PM10 concentration using a tree-based machine learning approach
The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), bo...
| Main Authors: | , , , , |
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| Format: | Article |
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MDPI
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100704/ |
| _version_ | 1848863394004729856 |
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| author | Wan Nur Shaziayani Ul-Saufie, Ahmad Zia Mutalib, Sofianita Mohamad Noor, Norazian Zainordin, Nazatul Syadia |
| author_facet | Wan Nur Shaziayani Ul-Saufie, Ahmad Zia Mutalib, Sofianita Mohamad Noor, Norazian Zainordin, Nazatul Syadia |
| author_sort | Wan Nur Shaziayani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM10 concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM10 concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM10 concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution. |
| first_indexed | 2025-11-15T13:32:13Z |
| format | Article |
| id | upm-100704 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:32:13Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1007042023-09-15T07:56:07Z http://psasir.upm.edu.my/id/eprint/100704/ Classification prediction of PM10 concentration using a tree-based machine learning approach Wan Nur Shaziayani Ul-Saufie, Ahmad Zia Mutalib, Sofianita Mohamad Noor, Norazian Zainordin, Nazatul Syadia The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine learning algorithms—namely, decision tree (DT), boosted regression tree (BRT), and random forest (RF)—were applied for the prediction of PM10 in Kota Bharu, Kelantan. The results from these three methods were compared to find the best method to predict PM10 concentration for the next day by using the maximum daily data from January 2002 to December 2017. To this end, 80% of the data were used for training and 20% for validation of the models. The performance measure of the PM10 concentration was based on accuracy, sensitivity, specificity, and precision for RF, BRT, and DT, respectively, which indicates that these three models were developed effectively, and they are applicable in the prediction of other atmospheric environmental data. The best model to use in predicting the next day’s PM10 concentration classification was the random forest classifier, with an accuracy of 98.37, sensitivity of 97.19, specificity of 99.55, and precision of 99.54, but the result of the boosted regression tree was substantially different from the RF model, with an accuracy of 98.12, sensitivity of 97.51, specificity of 98.72, and precision of 98.71. The best model can assist local governments in providing early warnings to people who are at risk of acute and chronic health consequences from air pollution. MDPI 2022-03-29 Article PeerReviewed Wan Nur Shaziayani and Ul-Saufie, Ahmad Zia and Mutalib, Sofianita and Mohamad Noor, Norazian and Zainordin, Nazatul Syadia (2022) Classification prediction of PM10 concentration using a tree-based machine learning approach. Atmosphere, 13 (4). art. no. 538. pp. 1-11. ISSN 2073-4433 https://www.mdpi.com/2073-4433/13/4/538 10.3390/atmos13040538 |
| spellingShingle | Wan Nur Shaziayani Ul-Saufie, Ahmad Zia Mutalib, Sofianita Mohamad Noor, Norazian Zainordin, Nazatul Syadia Classification prediction of PM10 concentration using a tree-based machine learning approach |
| title | Classification prediction of PM10 concentration using a tree-based machine learning approach |
| title_full | Classification prediction of PM10 concentration using a tree-based machine learning approach |
| title_fullStr | Classification prediction of PM10 concentration using a tree-based machine learning approach |
| title_full_unstemmed | Classification prediction of PM10 concentration using a tree-based machine learning approach |
| title_short | Classification prediction of PM10 concentration using a tree-based machine learning approach |
| title_sort | classification prediction of pm10 concentration using a tree-based machine learning approach |
| url | http://psasir.upm.edu.my/id/eprint/100704/ http://psasir.upm.edu.my/id/eprint/100704/ http://psasir.upm.edu.my/id/eprint/100704/ |