Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models

Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 , namely PM10. Statistical modellings are required to predict f...

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Main Author:	Japeri, Ahmad Zia Ul-Saufie Mohamad
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TA1-2040 Engineering (General) Civil engineering (General)
Online Access:	http://eprints.usm.my/45032/1/Ahmad%20Zia%20Ul-Saufie%20Mohamad%20Japeri24.pdf
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id	my-usm-ep.45032
record_format	uketd_dc
spelling	my-usm-ep.450322019-07-23T08:08:37Z Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models 2013-07 Japeri, Ahmad Zia Ul-Saufie Mohamad TA1-2040 Engineering (General). Civil engineering (General) Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 , namely PM10. Statistical modellings are required to predict future PM10 concentrations. The aims of this study are to develop and predict future PM10 concentration for next day (D+1), next two-days (D+2) and next three days (D+3) in seven selected monitoring stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010. Three main models for predicting PM10 concentration i.e. multiple linear regression, artificial neural network and hybrid models were used. The methods which were used in multiple linear regression were multiple linear regression (MLR), robust regression (RR) and quantile regression (QR), while feedforward backpropagation (FFBP) and general regression neural network (GRNN) were used in artificial neural network. Hybrid models are combination of principal component analysis (PCA) with all five prediction methods i.e. PCA-MLR, PCA-QR, PCA-RR, PCA-FFBP and PCA-GRNN. Results from the regression models show that RR and QR are better than the MLR method and they can act as an alternative method when assumption for MLR is not satisfied. The models for artificial neural network show that FFBP is better than the GRNN. Hybrid models gave better results compared to the single models in term of accuracy and error. Lastly, a new predictive tool for future PM10 concentration was developed using ten models for each site with average accuracy for D+1(0.7930), D+2 (0.6926) and D+3 (0.6410). This application will help local authority to take proper action to reduce PM10 concentration and as early warning system. 2013-07 Thesis http://eprints.usm.my/45032/ http://eprints.usm.my/45032/1/Ahmad%20Zia%20Ul-Saufie%20Mohamad%20Japeri24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Awam
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TA1-2040 Engineering (General) Civil engineering (General)
spellingShingle	TA1-2040 Engineering (General) Civil engineering (General) Japeri, Ahmad Zia Ul-Saufie Mohamad Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
description	Particulate matter has significant effect to human health when the concentration level of this substance exceeds Malaysia Ambient Air Quality Guidelines. This research focused on particulate matter with aerodynamic diameter less than 10 , namely PM10. Statistical modellings are required to predict future PM10 concentrations. The aims of this study are to develop and predict future PM10 concentration for next day (D+1), next two-days (D+2) and next three days (D+3) in seven selected monitoring stations in Malaysia which are represented by fourth different types of land uses i.e. industrial (three sites), urban (three sites), a sub-urban site and a reference site. This study used daily average monitoring record from 2001 to 2010. Three main models for predicting PM10 concentration i.e. multiple linear regression, artificial neural network and hybrid models were used. The methods which were used in multiple linear regression were multiple linear regression (MLR), robust regression (RR) and quantile regression (QR), while feedforward backpropagation (FFBP) and general regression neural network (GRNN) were used in artificial neural network. Hybrid models are combination of principal component analysis (PCA) with all five prediction methods i.e. PCA-MLR, PCA-QR, PCA-RR, PCA-FFBP and PCA-GRNN. Results from the regression models show that RR and QR are better than the MLR method and they can act as an alternative method when assumption for MLR is not satisfied. The models for artificial neural network show that FFBP is better than the GRNN. Hybrid models gave better results compared to the single models in term of accuracy and error. Lastly, a new predictive tool for future PM10 concentration was developed using ten models for each site with average accuracy for D+1(0.7930), D+2 (0.6926) and D+3 (0.6410). This application will help local authority to take proper action to reduce PM10 concentration and as early warning system.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Japeri, Ahmad Zia Ul-Saufie Mohamad
author_facet	Japeri, Ahmad Zia Ul-Saufie Mohamad
author_sort	Japeri, Ahmad Zia Ul-Saufie Mohamad
title	Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
title_short	Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
title_full	Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
title_fullStr	Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
title_full_unstemmed	Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models
title_sort	pm10 concentrations short term prediction using regression, artificial neural network and hybrid models
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Awam
publishDate	2013
url	http://eprints.usm.my/45032/1/Ahmad%20Zia%20Ul-Saufie%20Mohamad%20Japeri24.pdf
_version_	1747821441429012480

Pm10 Concentrations Short Term Prediction Using Regression, Artificial Neural Network And Hybrid Models

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