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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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Main Author: Hamid, Nur Haziqah Mohd
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2017
Subjects:
T Technology
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.usm.my/52156/1/Prediction%20Of%20PM10%20Using%20Multiple%20Linear%20Regression%20And%20Boosted%20Regression%20Trees_Nur%20Haziqah%20Mohd%20Hamid_A9_2017.pdf
http://eprints.usm.my/52156/
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spelling my.usm.eprints.52156 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)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology
TA Engineering (General). Civil engineering (General)
Hamid, Nur Haziqah Mohd
Prediction Of PM10 Using Multiple Linear Regression And Boosted Regression Trees
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).
format Monograph
author Hamid, Nur Haziqah Mohd
author_facet Hamid, Nur Haziqah Mohd
author_sort Hamid, Nur Haziqah Mohd
title 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_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_sort prediction of pm10 using multiple linear regression and boosted regression trees
publisher Universiti Sains Malaysia
publishDate 2017
url http://eprints.usm.my/52156/1/Prediction%20Of%20PM10%20Using%20Multiple%20Linear%20Regression%20And%20Boosted%20Regression%20Trees_Nur%20Haziqah%20Mohd%20Hamid_A9_2017.pdf
http://eprints.usm.my/52156/
_version_ 1729703923126632448
score 13.211869

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