Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees
Particulate matter with an aerodynamic diameter less than 10μm (PM10) is one of the pollutants that can adversely affect human health. The aims of this study is to predict particulate matter concentration for the next day (PM10D1) by using Multiple Linear Regression (MLR) and Boosted Regression Tree...
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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2017
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| Online Access: | http://eprints.usm.my/52156/ http://eprints.usm.my/52156/1/Prediction%20Of%20PM10%20Using%20Multiple%20Linear%20Regression%20And%20Boosted%20Regression%20Trees_Nur%20Haziqah%20Mohd%20Hamid_A9_2017.pdf |
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| author | Hamid, Nur Haziqah Mohd |
| author_facet | Hamid, Nur Haziqah Mohd |
| author_sort | Hamid, Nur Haziqah Mohd |
| building | USM Institutional Repository |
| collection | Online Access |
| description | Particulate matter with an aerodynamic diameter less than 10μm (PM10) is one of the pollutants that can adversely affect human health. The aims of this study is to predict particulate matter concentration for the next day (PM10D1) by using Multiple Linear Regression (MLR) and Boosted Regression Trees (BRT) models. The daily mean data used from 2013 until 2015 is divided into training data (70%) and validation data (30%). The parameters that influence PM10 concentration for the next day are particulate matter (PM10D0), wind speed (WS), temperature (T), relative humidity (RH), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO). Daily mean data were selected at four monitoring stations which are Jerantut (background station), Nilai (industrial area), Seberang Jaya (sub-urban area) and Shah Alam (urban area). The results obtained shows that Nilai station recorded the highest mean value of PM10 concentration compared to other stations. The main contributions of air pollution at Nilai station are particulate matter (PM10D0), carbon monoxide, nitrogen dioxide and ozone. The result shows that Multiple Linear Regression models (MLR) is the better model to predict the next day of PM10 concentration compared to Boosted Regression Trees (BRT). |
| first_indexed | 2025-11-15T18:30:49Z |
| format | Monograph |
| id | usm-52156 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T18:30:49Z |
| publishDate | 2017 |
| publisher | Universiti Sains Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-521562022-04-04T01:56:06Z http://eprints.usm.my/52156/ Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees Hamid, Nur Haziqah Mohd T Technology TA Engineering (General). Civil engineering (General) Particulate matter with an aerodynamic diameter less than 10μm (PM10) is one of the pollutants that can adversely affect human health. The aims of this study is to predict particulate matter concentration for the next day (PM10D1) by using Multiple Linear Regression (MLR) and Boosted Regression Trees (BRT) models. The daily mean data used from 2013 until 2015 is divided into training data (70%) and validation data (30%). The parameters that influence PM10 concentration for the next day are particulate matter (PM10D0), wind speed (WS), temperature (T), relative humidity (RH), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO). Daily mean data were selected at four monitoring stations which are Jerantut (background station), Nilai (industrial area), Seberang Jaya (sub-urban area) and Shah Alam (urban area). The results obtained shows that Nilai station recorded the highest mean value of PM10 concentration compared to other stations. The main contributions of air pollution at Nilai station are particulate matter (PM10D0), carbon monoxide, nitrogen dioxide and ozone. The result shows that Multiple Linear Regression models (MLR) is the better model to predict the next day of PM10 concentration compared to Boosted Regression Trees (BRT). Universiti Sains Malaysia 2017-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/52156/1/Prediction%20Of%20PM10%20Using%20Multiple%20Linear%20Regression%20And%20Boosted%20Regression%20Trees_Nur%20Haziqah%20Mohd%20Hamid_A9_2017.pdf Hamid, Nur Haziqah Mohd (2017) Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Awam. (Submitted) |
| spellingShingle | T Technology TA Engineering (General). Civil engineering (General) Hamid, Nur Haziqah Mohd Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees |
| title | Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees |
| title_full | Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees |
| title_fullStr | Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees |
| title_full_unstemmed | Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees |
| title_short | Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees |
| title_sort | prediction of pm10 using multiple linear regression and boosted regression trees |
| topic | T Technology TA Engineering (General). Civil engineering (General) |
| url | http://eprints.usm.my/52156/ http://eprints.usm.my/52156/1/Prediction%20Of%20PM10%20Using%20Multiple%20Linear%20Regression%20And%20Boosted%20Regression%20Trees_Nur%20Haziqah%20Mohd%20Hamid_A9_2017.pdf |