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Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli

Where air pollution control is concerned, the rare (extreme) event is typically more significant than the common event. Conventionally, a theory developed to address questions relating to the distribution of extremes is the Extreme Value Theory (EVT). The Bayesian approach offers a more comprehensib...

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Main Authors: Ahmat, Hasfazilah, Yahaya, Ahmad Shukri, Ramli, Nor Azam
Format: Article
Language:English
Published: Universiti Teknologi MARA, Pulau Pinang & Pusat Penerbitan Universiti (UPENA) 2016
Subjects:
Environmental pollution
Online Access:http://ir.uitm.edu.my/id/eprint/15330/1/AJ_HASFAZILAH%20AHMAT%20EAJ%2016.pdf
http://ir.uitm.edu.my/id/eprint/15330/
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spelling my.uitm.ir.153302020-03-20T02:28:30Z http://ir.uitm.edu.my/id/eprint/15330/ Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli Ahmat, Hasfazilah Yahaya, Ahmad Shukri Ramli, Nor Azam Environmental pollution Where air pollution control is concerned, the rare (extreme) event is typically more significant than the common event. Conventionally, a theory developed to address questions relating to the distribution of extremes is the Extreme Value Theory (EVT). The Bayesian approach offers a more comprehensible framework in incorporating all of the uncertainties involved in the prediction process using the conventional methods. To evaluate the performances of the classical and Bayesian approaches using non-informative priors in estimating the parameters of the Generalized Extreme Value (GEV) and to attain the best model to predict PM10 concentrations level. The daily maximum monitoring records of PM10 from January 2000 to December 2012 furnished by the Department of Environment, Malaysia were used in this study. The goodness-of-fit of the distribution was determined using performance indicators, namely, the accuracy measures and error measures. The best distribution was selected based on the highest accuracy measures and the smallest error measures. This study revealed that the Bayesian GEV with non-informative prior gave the best estimators for PM10 concentrations in three industrial monitoring stations and it could be applied in the PM10 analysis to predict the exceedances of future extreme concentrations of PM10 in these stations Universiti Teknologi MARA, Pulau Pinang & Pusat Penerbitan Universiti (UPENA) 2016 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/15330/1/AJ_HASFAZILAH%20AHMAT%20EAJ%2016.pdf Ahmat, Hasfazilah and Yahaya, Ahmad Shukri and Ramli, Nor Azam (2016) Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli. ESTEEM Academic Journal, 12 (1). pp. 1-10. ISSN 1675-7939
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Environmental pollution
spellingShingle Environmental pollution
Ahmat, Hasfazilah
Yahaya, Ahmad Shukri
Ramli, Nor Azam
Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
description Where air pollution control is concerned, the rare (extreme) event is typically more significant than the common event. Conventionally, a theory developed to address questions relating to the distribution of extremes is the Extreme Value Theory (EVT). The Bayesian approach offers a more comprehensible framework in incorporating all of the uncertainties involved in the prediction process using the conventional methods. To evaluate the performances of the classical and Bayesian approaches using non-informative priors in estimating the parameters of the Generalized Extreme Value (GEV) and to attain the best model to predict PM10 concentrations level. The daily maximum monitoring records of PM10 from January 2000 to December 2012 furnished by the Department of Environment, Malaysia were used in this study. The goodness-of-fit of the distribution was determined using performance indicators, namely, the accuracy measures and error measures. The best distribution was selected based on the highest accuracy measures and the smallest error measures. This study revealed that the Bayesian GEV with non-informative prior gave the best estimators for PM10 concentrations in three industrial monitoring stations and it could be applied in the PM10 analysis to predict the exceedances of future extreme concentrations of PM10 in these stations
format Article
author Ahmat, Hasfazilah
Yahaya, Ahmad Shukri
Ramli, Nor Azam
author_facet Ahmat, Hasfazilah
Yahaya, Ahmad Shukri
Ramli, Nor Azam
author_sort Ahmat, Hasfazilah
title Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
title_short Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
title_full Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
title_fullStr Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
title_full_unstemmed Prediction of PM10 extreme concentrations in selected industrial monitoring stations in Malaysia using Extreme Value Distribution (EVD): Classical and bayesian approaches / Hasfazilah Ahmat, Ahmad Shukri Yahaya and Nor Azam Ramli
title_sort prediction of pm10 extreme concentrations in selected industrial monitoring stations in malaysia using extreme value distribution (evd): classical and bayesian approaches / hasfazilah ahmat, ahmad shukri yahaya and nor azam ramli
publisher Universiti Teknologi MARA, Pulau Pinang & Pusat Penerbitan Universiti (UPENA)
publishDate 2016
url http://ir.uitm.edu.my/id/eprint/15330/1/AJ_HASFAZILAH%20AHMAT%20EAJ%2016.pdf
http://ir.uitm.edu.my/id/eprint/15330/
_version_ 1685648645228068864
score 13.211869

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