• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.104-104
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• 2012

최근 기후변화에 의하여 기상현상이 급변하고 있는 추세이며 강우사상의 경향 또한 그러한 변화를 따라가고 있다. 이러한 시점에서 극적인 강우사상에 대하여 대비해야 할 필요성이 대두되고 있으며 빈도해석을 통하여 확률강우량을 제시하는 방법이 연구되고 많은 발전을 거듭하고 있다. 이러한 방법은 모든 설계에 대하여 보편적으로 적용되고 있지만 일변량 빈도해석을 통하여 얻게 되는 확률량(Quantile)은 한 가지 자료계열에 대하여서만 고려할 수 있다. 이러한 단점을 극복하기 위하여서는 다변량 빈도해석을 수행하는 방법이 있으며 이 또한 국내외적으로 활발히 연구되고 있는 분야이다. 본 연구에서는 이변량 빈도해석을 수행하기 위해 3가지의 copula 모형을 선택하였으며 강우량과 강우지속시간을 자료계열로 사용하여 이변량 빈도해석을 수행하였다. 이를 통하여 얻은 확률강우량을 기존의 일변량 빈도해석의 결과와 정량적으로 비교하여 그 결과를 비교 분석하였으며 향후 새로운 빈도해석 방법의 가능성 및 적절성을 판단하고자 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.375-375
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• 2012

우리나라는 연강우량의 여름철 집중현상이 뚜렷하며 많은 부분이 태풍에 기인한다. 기후변화로 인하여 최근 들어서 태풍이 수반하는 폭우나 국지성 호우로 인한 강우사상이 증가하고 있어 짧은 시간에 많은 강우량이 발생하여 단기강우의 강도가 증가하고 있다. 이로 인하여 단기간에 예측하기 힘든 큰 강우량이 발생하는 경우가 빈번하여 이와 같은 강우에 의한 홍수를 대비할 필요성이 대두되고 있다. 따라서 본 연구에서는 태풍으로 인한 강우에 대하여 빈도해석을 수행하여 태풍으로 인하여 발생하는 확률강우량을 산정하였다. 태풍은 여러 인자를 포함하고 있는데 강우(1시간, 24시간, 총합), 풍속(최대, 순간최대), 중심최저기압, 중심최대풍속 등이 그것들이며, 강우와 동시에 그 이외의 인자들을 고려하기 위하여 이변량 빈도해석 모형인 copula 모형을 이용하여 빈도해석을 수행하였다. 이와 같이 copula 모형이 구성되면, 조건부 copula의 개념을 이용하여 강우 이외의 인자가 주어졌을 경우의 확률강우량을 산정할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.82-82
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• 2017

최근 기후변화로 인해 전 세계적으로 과거와 다른 이상홍수 발생이 빈번하게 발생하여 오래된 수공구조물인 댐, 저수지 붕괴가 우려되는 실정이다. 수공구조물의 수문학적인 안정성을 고려하지 않은 상황에서 댐 붕괴 홍수나 돌발홍수로 발생한 피해는 인명, 재산 및 환경 피해의 정도가 매우 크므로 피해가 발생하기 이전인 수공구조물 설계 시 홍수위험도 평가를 통해 안정성을 확보하는 것이 필요하다. 본 연구에서는 홍수사상의 다양한 변량들의 특성을 고려한 빈도해석을 위하여 Copula 함수를 이용한 다변량 빈도해석 기법을 개발하였다. 즉, 기존 홍수위험도 분석에서 주로 사용되는 첨두홍수량 뿐만 아니라, 홍수지속시간, 홍수체적 등을 고려한 이변량 또는 삼변량 홍수 빈도해석을 수행하고, 기존 홍수위험도와 비교 검토를 수행하고자 한다. 매개변수의 불확실성을 고려하기 위하여 매개변수 추정은 Bayesian 기법을 활용하였다.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.357-367
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• 2021

In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.1-15
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• 2017

Information on radar rainfall with high spatio-temporal resolution over large areas has been used to mitigate climate-related disasters such as flash floods. On the other hand, a well-known problem associated with the radar rainfall using the Marshall-Palmer relationship is the underestimation. In this study, we develop a new bias correction scheme based on the quantile regression method. This study employed a bivariate copula function method for the joint simulation between radar and ground gauge rainfall data to better characterize the error distribution. The proposed quantile regression based bias corrected rainfall showed a good agreement with that of observed. Moreover, the results of our case studies suggest that the copula function approach was useful to functionalize the error distribution of radar rainfall in an effective way.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.863-876
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• 2016

Climate change projections for precipitation are in general provided at daily time step. However, sub-daily precipitation data is necessarily required for hydrologic design and management. Thus, a reliable downscaling model is needed to analyze impact of climate change on water resources. While daily downscaling models have been widely developed and applied in hydrologic and climate community, hourly downscaling models have not been properly developed. In this regard, this study aims at developing a hourly downscaling model that can better reproduce sub-daily extreme rainfalls using conditional copula model. The proposed model was applied to generate extreme rainfalls under the RCP 8.5 scenario for weather stations in South Korea, and design rainfalls were then finally provided. We expected that the future design rainfalls can be used for baseline data to evaluate impact of climate change on water resources.

• Bulletin of the Korean Mathematical Society
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• v.47 no.3
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• pp.513-526
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• 2010

In this paper, the theory of n-ary hypergroups and some applications of hyperalgebras (Fredholm-Voltra integral, copula) are studied. We define some new concepts of topological hyperdynamical systems, universal hyperdynamical systems and immersed universal hyperalgebra. Also, we present some results in this respect.

• Journal of the Chungcheong Mathematical Society
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• v.24 no.4
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• pp.795-805
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• 2011

In this paper, we study a family of continuous bivariate distributions that possesses the negative quadrant dependence property and the generalized negatively quadrant dependent F-G-M copula. We also develop the partial ordering of this new parametric family of negative quadrant dependent distributions.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.193-225
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• 2006

The purpose of this paper is to analyze contagion in Asian foreign exchange markets using Extreme Value Theory and Copula. Our application deals with asymptotic dependence of daily exchange rate return for a sample of eight countries over period 1997.1.1-2005.4.13. The empirical results are summarized as follows. Firstly, Gumbel Copula is a good model to our data according to the value of AIC. Secondly, the extremal dependence between East Asian crisis countries became lower in the post crisis period than the crisis period. Thirdly, It seemed that high extremal dependence exists between East Asian countries with Singapore. Fourthly, the tail dependence between Indonesia, Malaysia, Thailand, Philippine became higher in the crisis period than the total period and post crisis period. Fifthly, the fact that the extremal dependence between Korea and Indonesia, Malaysia, Thailand, Philippine did not increase during the Asian Financial Crisis showed that the contagion effect was not the reason of the Korea's Fiancial Crisis. Sixthly, the extremal dependence between Asian exchange markets was not very high while comparing with the European exchange markets.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.747-759
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• 2018

Multivariate regional frequency analysis has advantages of regional and multivariate framework as adopting a large number of regional dataset and modeling phenomena that cannot be considered in the univariate frequency analysis. To the best of our knowledge, the multivariate regional frequency analysis has not been employed for hydrological variables in South Korea. Applicability of the multivariate regional frequency analysis should be investigated for the hydrological variable in South Korea in order to improve our capacity to model the hydrological variables. The current study focused on estimating parameters of regional copula and regional marginal models, selecting the most appropriate distribution models, and estimating regional multivariate growth curve in the multivariate regional frequency analysis. Annual maximum rainfall and duration data observed at 71 stations were used for the analysis. The results of the current study indicate that Frank and Gumbel copula models were selected as the most appropriate regional copula models for the employed regions. Several distributions, e.g. Gumbel and log-normal, were the representative regional marginal models. Based on relative root mean square error of the quantile growth curves, the multivariate regional frequency analysis provided more stable and accurate quantiles than the multivariate at-site frequency analysis, especially for long return periods. Application of regional frequency analysis in bivariate rainfall-duration analysis can provide more stable quantile estimation for hydraulic infrastructure design criteria and accurate modelling of rainfall-duration relationship.