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

Annazirin Eli, Mardhiyyah Shaffie, Wan Zawiah Wan Zin @ Wan Ibrahim

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1403-1410
Number of pages8
JournalSains Malaysiana
Volume41
Issue number11
Publication statusPublished - Nov 2012

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Markov chain
rainfall
simulation
analysis
method
modeling

Keywords

  • Annual maximum series
  • Bayesian MCMC
  • Extreme rainfall analysis
  • Extreme value distribution
  • Generalized maximum likelihood

ASJC Scopus subject areas

  • General

Cite this

Preliminary study on bayesian extreme rainfall analysis : A case study of Alor Setar, Kedah, Malaysia. / Eli, Annazirin; Shaffie, Mardhiyyah; Wan Zin @ Wan Ibrahim, Wan Zawiah.

In: Sains Malaysiana, Vol. 41, No. 11, 11.2012, p. 1403-1410.

Research output: Contribution to journalArticle

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