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Estimation of probability precipitation for the design of hydraulics structure is an important factor in determining the strength and scale. Generally, we use probability precipitation through the assumption of stationarity. Regional impact of climate change causes the necessity of the nonstationary probability rainfall estimation. Several nonlinear regression methods were applied to deal with the nonstationary behavior of extreme rainfall effectively by modeling the variability of the location and scale parameters of future extreme probability distribution. These regression models have drawback of forcing future change shape according to the regression types. In this study, the variability of the probable maximum rainfall for different durations in target years at Seoul was analyzed with different approaches such as logistic regression, power regression, and neural networks using GCM data associated with different green house gas emission scenarios. The increasing ratio of the future hourly probability rainfall using NN is larger than that using Logistic and Power models, and the increasing ratio of the future 24hr probability rainfall using NN is smaller than that using Logistic and Power models.

Nonstationary Probability Rainfall Estimation at Seoul Using Neural Networks and GCM Data