• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.257-267
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• 2010

A flood event can be characterized by three attributes such as peak discharge, total flood volume, and flood duration, which are correlated each other. However, the amount of peak discharge is only used to evaluate the flood events for the hydrological plan and design. The univariate analysis has a limitation in describing the complex probability behavior of flood events. Thus, the univariate analysis cannot derive satisfying results in flood frequency analysis. This study proposed bivariate flood frequency analysis methods for evaluating flood events considering correlations among attributes of flood events. Parametric distributions such as Gumbel mixed model and bivariate gamma distribution, and a non-parametric model using a bivariate kernel function were introduced in this study. A time series of annual flood events were extracted from observations of inflow to the Soyang River Dam and the Daechung Dam, respectively. The joint probability distributions and return periods were derived from the relationship between the amount of peak discharge and the total volume of flood runoff. Applicabilities of bivariate flood frequency analysis were examined by comparing the return period acquired from the proposed bivariate analyses and the conventional univariate analysis.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.141-149
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• 2013

We present methods for studying the log-density ratio that enables the selection of the predictors and the form to be included in the logistic regression model. Under bivariate normal distributional assumptions, we investigate the form of the log-density ratio as a function of two predictors. If two covariance matrices are equal, then the crossproduct and quadratic terms are not needed. If the variables are uncorrelated, we do not need the crossproduct terms, but we still need the linear and quadratic terms. We also explore other conditions in which the crossproduct and quadratic terms are not needed in the logistic regression model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.599-606
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• 2010

Due to the short period of precipitation data in Korea, the uncertainty of drought analysis is inevitable from a point frequency analysis. So it is desired to introduce a regional drought frequency analysis. This study first extracted drought characteristics from 3-month and 12-month moving average rainfalls which represent short and long-term droughts, respectively. Then, the homogeneous regions were distinguished by performing a principal component analysis and cluster analysis. The Korean peninsula was classified into five regions based on drought characteristics. Finally, this study applied the bivariate frequency analysis using a kernel density function to quantify the regionalized drought characteristics. Based on the bivariate drought frequency curves, the drought severities of five regions were evaluated for durations of 2, 5, 10, and 20 months, and return periods of 5, 10, 20, 50, and 100 years. As a result, the largest severity of drought was occurred in the Lower Geum River basin, in the Youngsan River basin, and over in the southern coast of Korea.