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/ http://ir.uitm.edu.my/id/eprint/15330/1/AJ_HASFAZILAH%20AHMAT%20EAJ%2016.pdf

id	uitm-15330
recordtype	eprints
spelling	uitm-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
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/ http://ir.uitm.edu.my/id/eprint/15330/1/AJ_HASFAZILAH%20AHMAT%20EAJ%2016.pdf
first_indexed	2023-09-18T22:53:36Z
last_indexed	2023-09-18T22:53:36Z
_version_	1777417707172397056

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

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