The consequences of statistical modeling of extreme rainfall are pivotal for civil engineering and planning division in order to instill the capability of structures of building that can withstand the extreme situation. Yearly maximum rainfall data of Karachi, Badin, Chhor from
1961-2010, and Rohri from 1971- 2010 have been used in this study. The method of Maximum Likelihood (ML) and Bayesian techniques have been implemented to estimate the parameters of Generalized
Extreme Value (GEV) distribution and also to compute return levels against sundry return periods. Non-informative priors are used to get the posterior densities. To gauge and compare the results of
the above mentioned methods, acceptance rates and forecasting errors have been used as Goodness of Fit (GoF) test. Though both the methods are applicable, but the GoF test highlights that M L method
is slightly better than Bayesian for observing the annual maximum rainfall in Sindh province of Pakistan.
Muhammad Ali, Muhammad Jawed Iqba, , and Zohaib Aziz. (2016) Application of Bayesian Monte Carlo Technique to Calculate Extreme Rainfall over Sindh Province in Comparison with Maximum Likelihood Method, , Proc. of the PAS: A; 53,, Issue 2.
