This study presented how to evaluate the inland inundation risk considering the characteristics of inland flood. Fuzzy AHP (Analytic Hierarchy Process), which can deal with the uncertainty or ambiguousness of the decision-making process, was used to estimate the inundation risk. The criteria used for inland inundation risk include the physical index, social index and inland flood. Each index contains three detailed indicators then total nine indicators were employed in this study. The inundation risk evaluation was carried out for each node (manhole) within the drainage system, not to the administrative extent, which enabled us to point out nodes with high risk. The proposed Fuzzy AHP was applied to Geoje district in Busan. The results indicated that the junction of Oncheoncheon and Geojecheon has high risk which is consistent with the fact that this junction has already experienced floods in the past. The proposed method can be used for evaluating inland inundation risk and preparing flood prevention plans in inland flood-prone urban areas.

In South Korea, stationary frequency analysis methods are generally used for estimating design rainfalls in practice. However, due to climate change and/or variability, recent rainfall observations have significantly different patterns from the past so that the recent trends need to be considered to estimate extreme rainfall quantiles for hydrologic design. This study focused on estimating extreme rainfall quantiles in administrative districts across South Korea, after building nonstationary GEV model using annual maximum rainfall (AMR) datasets for 228 administrative districts from point rainfall measures from 1973 to 2012. A moving average method with 25-year window was used for investigating time-dependent statistics of AMR, such as mean, variance and skewness, and parameters of GEV distribution. From the analyses of relationships between statistics and distribution parameters, this study derived nonlinear regression equations for distribution parameters, which provide the estimates of distribution parameters at any future time. The overall results achieved in this study illustrate that the nonlinear regression equations can be easily incorporated into the hydrologic frequency analysis and provide appropriate estimates of design rainfalls in the near future.