Parameter estimation of extreme rainfall distribution in Johor using Bayesian Markov Chain Monte Carlo

Abstract

Heavy rainfall and the associated floods occur frequently in the Malaysia and have caused huge economic losses as well as massive impact on agriculture and people. As a consequence, it is necessary to understand the distribution of extreme rainfall in order to improve the managements in a country. Thus, the aim of this paper is to determine the best method to estimate parameters of Generalized Extreme Value (GEV) distribution that represent the annual maximum series (AMS) data of daily rainfall by using method of moments (MOM), maximum likelihood estimators (MLE) and Bayesian Markov Chain Monte Carlo (MCMC). The daily precipitation rainfall amount of 12 rain gauge stations in Johor from year 1975 to 2008 were used and the AMS data of each year were fitted with GEV distribution. Based on goodness-of-fit tests, namely Relative Root Mean Square Error (RRMSE) and Relative Absolute Square Error (RASE), the performances of three parameters of GEV distribution estimated by MOM, MLE and Bayesian MCMC were compared for each station. The results indicated that Bayesian MCMC method was performed better than MOM and MLE method in estimating the parameters of GEV distribution.