Abstract
The NSRPM(Neyman-Scott Rectangular Pulse Model), a stochastic rainfall generation model using clustered point process theory, is widely used in hydrology since it is able to reflect the cluster characteristics of rainfall events but the RPM(Rectangular Pulse Model) is not able to handle that. DFP(Davidon-Flectcher-Powell) and GA(Genetic Algorithm) are generally used to estimate 5 model parameters of the NSRPM. However DFP is sensitive to initial value and has tendency to get not global solution but local minimization. GA has an advantage of optimizing without Hessian matrix, but it needs relatively long computing time. In this study, a DE(Differential Evolution) method applied in model parameter estimation in order to overcome those drawbacks. The rainfall model parameters during the summer season (June-August) at Seoul, Pusan, Daegu, Mokpo, Gangrung, which have more than 30 year long hourly precipitation data, were estimated using hourly rainfall data from 1961 to 2011. Results were compared to those using DFP and GA. The performance of the DE method was evaluated using test statistics and demonstrated better performance in preserving physical and statistical properties of observed rainfall data.
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