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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
http://eprints.usm.my/52217/
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spelling my.usm.eprints.52217 http://eprints.usm.my/52217/ Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods 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 NonPeerReviewed application/pdf en http://eprints.usm.my/52217/1/NORAZRIN%20BINTI%20RAMLI.pdf Ramli, Norazrin (2021) Short-Term Prediction Models Of Pm10 Concentrations In Peninsular Malaysia Using Multivariate Time Series And Machine Learning Methods. PhD thesis, Perpustakaan Hamzah Sendut.
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 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
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
publishDate 2021
url http://eprints.usm.my/52217/1/NORAZRIN%20BINTI%20RAMLI.pdf
http://eprints.usm.my/52217/
_version_ 1729703931680915456
score 13.211869

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