Fitting daily rainfall distribution in peninsular Malaysia - Kong, Wen Gie (2022)
Heavy rains can affect daily living without any signs. On the other side, severe rainfall can result in major agricultural and fishery losses. We can anticipate rainfall characteristics and behaviours in advance by rainfall modelling. Consequently, the dis tribution of the rainfall are obtained to analysis the rainfall patterns and features. This project aims to find a good fit statistical distributions for five rainfall stations in Malaysia. Five rainfall stations are obtained from Malaysian Meoteology Depa rtment ranging from year 1968 to 2021. The statistical distributions that applied in this study are Gamma, Weibull, Tweedie family, Lognormal and Pareto distribution. Thus, the good fit distributions are determined by GoodnessGoodne ssofoffit (GOF) test. The fit (GOF) test that used in this study included the Kolmogorov Smirnov D statistic (KS), AndersonDarling statistic (AD), Chi-- square test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The result shows that the Gamma an d Lognormal distribution are appropriate distributions to describe the daily rainfall distribution in Peninsular Malaysia.