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This study presents the best fitting distribution to describe the siries MSI based on hourly rainfall form 1970 to 2008 for three rain gauge stations in Peninsular Malaysia namely Bertam, Dungun and Pekan. Two three-parameter extreme value distributions which are considered are Generalized Extreme Value (GEV) and Generalized Logistic (GL). The parameters of these distributions are determined using the Bayesian MCMC with non-informative prior distribution and L-moments (LMOM) method. The Goodness-Of-Fit (GOF) between empirical data and theoretical distributions are then evaluated for each stations. The result show that the majority of the stations are found that the L-moment method can give the best modelling for MSI, specified for GEV distribution. Based on the model that has been identified, we can reasonably predict the risks associated the MSI for various return periods.

Modeling of Annual Maximum Storm Intensity with Bayesian Markov Chain Monte Carlo (MCMC) and L-moment