Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting
The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR...
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2023
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my.uniten.dspace-263282023-05-29T17:09:09Z Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting Mansor A.A. Abdullah S. Dom N.C. Napi N.N.L.M. Ahmed A.N. Ismail M. Zulkifli M.F.R. 57211858557 56509029800 57217286875 57224902975 57214837520 57210403363 57221126643 The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area. � 2021 WITPress. All rights reserved. Final 2023-05-29T09:09:09Z 2023-05-29T09:09:09Z 2021 Article 10.18280/ijdne.160107 2-s2.0-85103438754 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103438754&doi=10.18280%2fijdne.160107&partnerID=40&md5=06231a5c328f50d7e319f8e3359defa5 https://irepository.uniten.edu.my/handle/123456789/26328 16 1 53 59 All Open Access, Bronze International Information and Engineering Technology Association Scopus |
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The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area. � 2021 WITPress. All rights reserved. |
| author2 |
57211858557 |
| author_facet |
57211858557 Mansor A.A. Abdullah S. Dom N.C. Napi N.N.L.M. Ahmed A.N. Ismail M. Zulkifli M.F.R. |
| format |
Article |
| author |
Mansor A.A. Abdullah S. Dom N.C. Napi N.N.L.M. Ahmed A.N. Ismail M. Zulkifli M.F.R. |
| spellingShingle |
Mansor A.A. Abdullah S. Dom N.C. Napi N.N.L.M. Ahmed A.N. Ismail M. Zulkifli M.F.R. Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting |
| author_sort |
Mansor A.A. |
| title |
Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting |
| title_short |
Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting |
| title_full |
Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting |
| title_fullStr |
Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting |
| title_full_unstemmed |
Three-hour-ahead of multiple linear regression (MLR) models for particulate matter (PM10) forecasting |
| title_sort |
three-hour-ahead of multiple linear regression (mlr) models for particulate matter (pm10) forecasting |
| publisher |
International Information and Engineering Technology Association |
| publishDate |
2023 |
| _version_ |
1806428234433167360 |
| score |
13.222552 |