Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia

Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar we...

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Main Authors: Annazirin Eli, Mardhiyyah Shaffie, Wan Zawiah Wan Zin
Format: Article
Language:English
Published: Universiti Kebangsaan Malaysia 2012
Online Access:http://journalarticle.ukm.my/5576/1/09%2520Annazirin.pdf
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author Annazirin Eli,
Mardhiyyah Shaffie,
Wan Zawiah Wan Zin,
author_facet Annazirin Eli,
Mardhiyyah Shaffie,
Wan Zawiah Wan Zin,
author_sort Annazirin Eli,
collection UKM
description Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters.
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spelling ukm.eprints-55762016-12-14T06:38:51Z http://journalarticle.ukm.my/5576/ Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia Annazirin Eli, Mardhiyyah Shaffie, Wan Zawiah Wan Zin, Statistical modeling of extreme rainfall is essential since the results can often facilitate civil engineers and planners to estimate the ability of building structures to survive under the utmost extreme conditions. Data comprising of annual maximum series (AMS) of extreme rainfall in Alor Setar were fitted to Generalized Extreme Value (GEV) distribution using method of maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. The weakness of ML method in handling small sample is hoped to be tackled by means of Bayesian MCMC simulations in this study. In order to obtain the posterior densities, non-informative and independent priors were employed. Performances of parameter estimations were verified by conducting several goodness-of-fit tests. The results showed that Bayesian MCMC method was slightly better than ML method in estimating GEV parameters. Universiti Kebangsaan Malaysia 2012-11 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/5576/1/09%2520Annazirin.pdf Annazirin Eli, and Mardhiyyah Shaffie, and Wan Zawiah Wan Zin, (2012) Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia. Sains Malaysiana, 41 (11). pp. 1403-1410. ISSN 0126-6039 http://www.ukm.my/jsm/
spellingShingle Annazirin Eli,
Mardhiyyah Shaffie,
Wan Zawiah Wan Zin,
Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia
title Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia
title_full Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia
title_fullStr Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia
title_full_unstemmed Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia
title_short Preliminary study on bayesian extreme rainfall analysis: a case study of Alor Setar, Kedah, Malaysia
title_sort preliminary study on bayesian extreme rainfall analysis a case study of alor setar kedah malaysia
url http://journalarticle.ukm.my/5576/1/09%2520Annazirin.pdf
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