Sandy Hardian Susanto Herho 

This study presents how Extreme Value Analysis (EVA) can be used to predict future extreme hydrological events and how dynamic-programming based change point detection algorithm can be used to detect the abrupt transition in discharge events variability in Kali Kupang, Central Java, Indonesia. By using the annual block maxima, we can predict the upper extreme discharge probability from the Gumbel distribution, which is the extreme distribution that best fits the data, after distribution fitting using the Markov Chain Monte Carlo (MCMC) method. Using the pruned exact linear time (PELT) algorithm, with a change point location, it is known that the annual standard deviation of this time series has changed in the mid-1990s. Despite some shortcomings, this study can pave the way for the use of non-traditional data analysis algorithms in analyzing hydrological time-series data in the Indonesian region.