Abstract
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.
Highlights
Statistical modeling of extreme event is important in various disciplines including hydrology, engineering and environmental science
For environmental processes extreme value theory can be used to estimate the probabilities of extreme levels of the processes
Extreme rainfall event is often associated with climate change, which may be followed by siries of natural disasters such as flash floods and landslides
Summary
Statistical modeling of extreme event is important in various disciplines including hydrology, engineering and environmental science. For environmental processes extreme value theory can be used to estimate the probabilities of extreme levels of the processes For some processes, such as sea-level and wind speed, this information can help in the design of structures such as sea walls, bridges and buildings. For other processes, such as rainfall and pollution, the information can be used to assess danger due to extreme levels of the process. Extreme value theory can be used in finance to, for example, assess the risks of large insurance claims or predict the probability of rare events. Extreme rainfall event is often associated with climate change, which may be followed by siries of natural disasters such as flash floods and landslides
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