Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods

The particulate matter with an aerodynamic diameter less than 10 μm (PM10) is identified as one of the dangerous air pollutants to human health and the concentrations of PM10 in Asian and Pacific cities remain as the most problematic local air pollution issues. The objectives of the research are to...

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Main Author:	Ramli, Norazrin
Format:	Thesis
Language:	English
Published:	2021
Subjects:	L Education (General)
Online Access:	http://eprints.usm.my/52217/1/NORAZRIN%20BINTI%20RAMLI.pdf
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id	my-usm-ep.52217
record_format	uketd_dc
spelling	my-usm-ep.522172022-04-06T08:08:50Z Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods 2021-05 Ramli, Norazrin L Education (General) The particulate matter with an aerodynamic diameter less than 10 μm (PM10) is identified as one of the dangerous air pollutants to human health and the concentrations of PM10 in Asian and Pacific cities remain as the most problematic local air pollution issues. The objectives of the research are to determine the characteristics and trend of PM10 concentrations in Malaysia from 1999 to 2015, to propose a Multivariate Time Series (MTS) analysis using Vector Autoregressive (VAR) to predict the short-term PM10 concentrations and interpret the relationship between PM10 concentrations and meteorological parameters using the graphical view of causality. Three models for short-term prediction of PM10 using Multiple Linear Regression (MLR), Bayesian Model Averaging (BMA) and Boosted Regression Tree (BRT) model. The performance indicators (R2, IA, MAE, RMSE, and MAPE) are applied to obtain the best model. A study using seventeen years of air quality monitoring data from the Department of Environment Malaysia (DOE) was used with eight parameters (PM10, NO2, SO2, CO, O3, wind speed, temperature, and relative humidity) and nine monitoring stations were selected which included Kangar, Perai, Shah Alam, Nilai, Larkin, Pasir Gudang, Kertih, Kota Bharu and Jerantut to represent the Northern, Central, Southern and East of Peninsular Malaysia. The trend analysis used the Mann-Kendall test for trend detection and Sen’s slope estimator for trend estimation using monthly average and maximum monthly of PM10 concentrations. 2021-05 Thesis http://eprints.usm.my/52217/ http://eprints.usm.my/52217/1/NORAZRIN%20BINTI%20RAMLI.pdf application/pdf en public phd doctoral Perpustakaan Hamzah Sendut Pusat Pengajian Pendidikan Jarak Jauh
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	L Education (General)
spellingShingle	L Education (General) Ramli, Norazrin Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
description	The particulate matter with an aerodynamic diameter less than 10 μm (PM10) is identified as one of the dangerous air pollutants to human health and the concentrations of PM10 in Asian and Pacific cities remain as the most problematic local air pollution issues. The objectives of the research are to determine the characteristics and trend of PM10 concentrations in Malaysia from 1999 to 2015, to propose a Multivariate Time Series (MTS) analysis using Vector Autoregressive (VAR) to predict the short-term PM10 concentrations and interpret the relationship between PM10 concentrations and meteorological parameters using the graphical view of causality. Three models for short-term prediction of PM10 using Multiple Linear Regression (MLR), Bayesian Model Averaging (BMA) and Boosted Regression Tree (BRT) model. The performance indicators (R2, IA, MAE, RMSE, and MAPE) are applied to obtain the best model. A study using seventeen years of air quality monitoring data from the Department of Environment Malaysia (DOE) was used with eight parameters (PM10, NO2, SO2, CO, O3, wind speed, temperature, and relative humidity) and nine monitoring stations were selected which included Kangar, Perai, Shah Alam, Nilai, Larkin, Pasir Gudang, Kertih, Kota Bharu and Jerantut to represent the Northern, Central, Southern and East of Peninsular Malaysia. The trend analysis used the Mann-Kendall test for trend detection and Sen’s slope estimator for trend estimation using monthly average and maximum monthly of PM10 concentrations.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ramli, Norazrin
author_facet	Ramli, Norazrin
author_sort	Ramli, Norazrin
title	Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
title_short	Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
title_full	Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
title_fullStr	Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
title_full_unstemmed	Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods
title_sort	short-term prediction models of pm10 concentrations in peninsular malaysia using multivariate time series and machine learning methods
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Pendidikan Jarak Jauh
publishDate	2021
url	http://eprints.usm.my/52217/1/NORAZRIN%20BINTI%20RAMLI.pdf
_version_	1747822147603005440

Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods

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