(untitled) (Malaysian Journal of Civil Engineering, 2018)
Sub-daily timescale data such as hourly data are needed for modeling urban systems. However such series are not readily available as compared to daily rainfall series. Stochastic rainfall models are useful in estimating input for design work. One of the models that applies the clustered point process theory is the Neyman-Scott Rectangular Pulses (NSRP) model. The model uses a flexible model fitting procedure which involves matching approximately a chosen set of historical statistics which exceeds in number of set of parameters to be estimated. An optimization technique called Shuffle Complex Evolution (SCE-UA) was used to estimate the parameters. The performance of NSRP model was evaluated using 10 years hourly data taken from a station in Wilayah Persekutuan. Two distributions, namely exponential (EXP) and mixed exponential (MEXP) were used to model the cell intensities in the model. The models were evaluated on a monthly basis regarding their ability to preserve the statistical properties as well as the physical properties of the rainfall time-series over timescales of 1 h, 6 h and 24 h. The performance of the models with the two different distributions was evaluated and compared. The model with the mixed exponential (MEXP) distribution perform better in preserving most of the statistical and physical properties of the observed data