Sains Malaysiana 41(11)(2012):
1403–1410
Preliminary Study on Bayesian Extreme Rainfall
Analysis: A Case Study
of Alor Setar, Kedah, Malaysia
(Kajian Awal bagi Analisis
Kehujanan Melampau Bayes: Kajian Kes
di Alor Setar,
Kedah, Malaysia)
Annazirin
Eli*
Department
of Science in Engineering, Faculty of Engineering
International
Islamic University Malaysia, 50728 Gombak, Kuala Lumpur, Malaysia
Mardhiyyah Shaffie & Wan
Zawiah
Wan Zin
School of Mathematical Siences, Faculty
of Science and Technology
Universiti Kebangsaan Malaysia, 43600
Bangi, Selangor, Malaysia
Diserahkan: 10 Oktober 2011 /
Diterima: 18 Jun 2012
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.
Keywords: Annual maximum series;
Bayesian MCMC; extreme rainfall
analysis; extreme value distribution; generalized maximum likelihood
ABSTRAK
Pemodelan
statistik bagi hujan melampau amat penting, memandangkan hasil dapatannya mampu
membantu jurutera awam dan pakar runding untuk menjangka kebolehan struktur
sesebuah bangunan untuk bertahan dalam situasi yang paling melampau. Data daripada siri maksimum tahunan (AMS)
disuaikan menggunakan taburan nilai melampau teritlak (GEV)
dengan menggunakan kaedah kebolehjadian maksimum (ML)
dan kaedah simulasi Markov Chain Monte Carlo (MCMC)
Bayes. Kelemahan kaedah ML dalam pengendalian sampel kecil diharap dapat diatasi dengan
kaedah simulasi MCMC Bayes. Bagi mendapatkan taburan posterior, taburan prior tak-bermaklumat
dan tak-bersandar digunakan. Padanan bagi parameter
yang dicadangkan disahkan dengan menjalankan beberapa ujian kebagusan penyuaian
(GOF). Hasilnya,
didapati kaedah MCMC Bayes
memberikan anggaran yang sedikit lebih baik berbanding kaedah ML bagi menganggar nilai-nilai parameter taburan GEV.
Kata kunci: Kaedah kebolehjadian maksimum; kajian hujan ekstrem; MCMC Bayes; siri
maksimum tahunan; taburan nilai ekstrim teritlak
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*Pengarang surat-menyurat;
email: annazirin@iium.edu.my
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