• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2017년도 학술발표회
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• pp.463-463
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• 2017

최근 다변량 확률모형 연구 및 기후변화에 따른 강우패턴 연구의 증가에 따라 시계열로 기록되어 있는 강우량 자료로부터 강우사상(Event)을 분리하는 연구 또한 활발히 이루어지고 있다. 일반적으로 강우사상은 최소무강우시간(Inter-Event Time)을 기준으로 전후강우가 독립적인 강우인지 연속적인 강우인지 구별하는데 이 최소무강우시간을 결정하는 방법이 각 사용되는 분야마다 일관되지 않은 점이 있다. 본 연구에서는 30년 이상 기록된 기상청 강우관측소 자료를 이용하였으며, 설계강우의 시간분포를 위한 Huff 4분위법에서 사용되는 6시간의 최소무강우시간분터 지수확률분포 방법으로 얻어지는 최소무강우시간(일반적으로 12시간 내외)까지 최소무강우시간의 변화에 따라 분리된 강우사상의 특성을 분석하였다. 또한 강우사상의 이변량 빈도해석 적합성을 검토하기 위해 연최대강우량 사상을 선정하여 빈도해석을 수행하였으며 최소무강우 시간에 따라 이변량 확률분포형 적합성을 검토하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2015년도 학술발표회
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• pp.414-414
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• 2015

최근 지구온난화로 인한 기상변동성 증가로 인해 극한기후현상의 발생빈도가 점차 증가하고 있으며 유역단위의 수자원을 효율적으로 운영하는데 문제점을 해소하고자 다양한 측면에서 체계적인 수자원 운영을 위한 연구가 이루어지고 있다. 수공구조물을 설계하는데 있어서 가장 일반적인 가정 사항은 수문모형에 사용되는 강우의 빈도와 유출의 빈도가 동일하다는 가정에 근거한다. 즉, 유역의 초기함수조건, 강우강도, 강우의 시간적 분포와 관계없이 동일한 빈도로 고려되는 문제점이 있다. 이러한 점에서 비교적 장기간의 자료를 확보하고 있는 계측유역에 대해서 다변량 확률밀도함수를 적용하여 비선형관계를 고려한 수문빈도해석기법을 개발하고자 한다. 본 연구에서는 이변량 분석기법(bivariate analysis) 중 전통적인 이변량 분포에 비해 주변분포형(marginal distribution)을 자유롭게 선택할 수 있는 장점이 있는 추계학적 Copula 모형을 활용하여 댐 및 저수지 상류유역의 강우량과 유입량을 대상으로 이변량 분석을 수행하고자 한다. 최종적으로 비선형 관계에 있는 강수량과 유출량 사이에 이변량 빈도해석 모형을 개발하고 기존 해석방법과의 종합적인 비교를 실시하였다.

• Journal of Korea Water Resources Association
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• 제51권9호
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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.

• Journal of Korea Water Resources Association
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• 제42권9호
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• pp.725-736
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• 2009

A flood event can be defined by three characteristics; peak discharge, total flood volume, and flood duration, which are correlated each other. However, a conventional flood frequency analysis for the hydrological plan, design, and operation has focused on evaluating only the amount of peak discharge. The interpretation of this univariate flood frequency analysis has a limitation in describing the complex probability behavior of flood events. This study proposed a bivariate flood frequency analysis using a Gumbel mixed model for the flood evaluation. A time series of annual flood events was extracted from observations of inflow to the Soyang River Dam and the Daechung Dam, respectively. The joint probability distribution and return period were derived from the relationship between the amount of peak discharge and the total volume of flood runoff. The applicability of the Gumbel mixed model was tested by comparing the return periods acquired from the proposed bivariate analysis and the conventional univariate analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2010년도 학술발표회
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• pp.600-604
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• 2010

수문설계 인자인 확률홍수량 산정시 짧은 홍수량 자료 길이로 인해 홍수량을 직접 이용하기 보다는 강우자료와 강우-유출모형에 의존하고 있는 현시점에서 무엇보다 중요한 것은 신뢰할 만한 확률강우량이 산정되어야 한다는 것이다. 하지만 지금까지의 강우빈도해석(rainfall frequency analysis)은 강도(intensity), 지속기간(duration), 깊이(depth) 사이의 연관성은 고려하지 않은 단편적인 방법론에 그치고 있다. 즉, 강우를 표현하는 인자들 간 독립(independency)이라는 가정을 거친 후, 간단한 단변량(univariate) 강우빈도분포(rainfall frequency distribution)로 확률강우량을 산정하고 있다는 것이다. 간단한 모형에 따른 이점은 있으나 최근의 강우 형태는 매우 복잡한 양상을 띠고 있어, 단변량 강우빈도분포로 이를 대표하기에는 무리가 따른다. 따라서 본 연구에서는 강우빈도해석의 인자가 독립적이며 정규분포(normal distribution)라 가정하지 않고, 세 개의 주변 분포(marginal distribution)의 형태가 같지 않다는 점, 또한 가정하지 않는 방법론 중, 그 가치를 널리 인정받고 있는 Archimedean Copula (AC)에 대한 연구를 수행하였다. AC를 이용하여 강도, 지속기간, 깊이 사이의 종속성 중, 두 가지 변량을 고려한 이변량(bivariate) 강우빈도해석을 수행하였고 그 효용성을 검토해 보았다.

• Environmental and Resource Economics Review
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• 제12권4호
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• pp.559-577
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• 2003

This paper analyzes household bottled water and water purifier expenditures, taking into account three important characteristics: expenditures may be censored at zero, may be interdependent across expenditure type, and may be endogenously and jointly determined. Censoring, interdependence, and endogeneity of the two expenditures are examined through simultaneous equation bivariate Tobit model. Expenditure function parameters are estimated using a 1997 household survey data collected in Seoul. The study detected interdependence between the two expenditures in the data. Moreover, the coefficient of one expenditure variable is statistically significant in the other expenditure equation. Thus, the overall results show that the simultaneous equation bivariate Tobit model employed here is appropriate for this analysis of the two expenditures. Finally estimated income and household size elasticities of the expenditures are presented.

• Communications for Statistical Applications and Methods
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• 제17권3호
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• pp.413-421
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• 2010

In this study, we considered bivariate conditional auto-regressive model taking into account spatial association as well as correlation between the two dependent variables, which are the counts of murder and burglary. We conducted likelihood ratio test for checking over-dispersion issues prior to applying spatial poisson models. For the real application, we used the annual counts of violent crimes at 25 districts of Seoul in 2007. The statistical results are visually illustrated by geographical information system.

• The Korean Journal of Applied Statistics
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• 제32권1호
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• pp.69-81
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• 2019

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models.

• Journal of Korea Water Resources Association
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• 제51권1호
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• pp.49-60
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• 2018

The multivariate regional frequency analysis has many advantages such as an adaption of regional parameters and consideration of a correlated structure of the data. The multivariate regional frequency analysis can provide the broader and more detailed information for the hydrological variables. The multivariate regional frequency analysis has not been attempted to model hydrological variables in South Korea yet. Therefore, it is required to investigate the applicability of the multivariate regional frequency analysis in the modeling of the hydrological variables. The current study investigated the applicability of the homogeneous region delineation and their characteristics in bivariate regional frequency analysis of annual maximum rainfall depth-duration data. The K-medoid method was employed as a clustering method. The discordancy and heterogeneous measures were used to assess the appropriateness of the delineation results. According to the results of the clustering analysis, the employed stations could be grouped into five regions. All stations at three of the five regions led to acceptable values of discordancy measures than the threshold. The stations where have short record length led to the large discordancy measures. All grouped regions were identified as a homogeneous region based on heterogeneous measure estimates. It was observed that there are strong cross-correlations among the stations in the same region.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1221-1229
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• 2014

In this paper we choose the best model among several bivariate Poisson models on Korean soccer data. The models considered allow for correlation between the number of goals of two competing teams. We use an R package called bivpois for bivariate Poisson regression models and the data of K-league for season 1983-2012. Finally we conclude that the best fitted model supported by the AIC and BIC is the bivariate Poisson model with constant covariance. The zero and diagonal inflated models did not improve the model fit. The model can be used to examine home-away effect, goodness of fit, attack and defense parameters.