• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.171-171
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• 2011

상수도의 합리적인 운용과 관리를 위해서는 급수량 예측이 매우 중요하다. 기존 급수량 예측은 신경망과 칼만 필터법을 사용한 연구들이 대부분이었다. 이러한 연구결과들은 높은 상관결과를 갖고 있지만 이는 자기상관계수에 대한 높은 의존도에 따른 결과로 볼 수 있다. 즉, 예측의 결과가 전날 수요량을 거의 그대로 따라오는 경향을 띄어, 급수량 예측 그래프가 기존 그래프를 오른쪽으로 이동시킨 것과 같이 나타난다. 본 연구에서는 이러한 문제점들을 해결하기 위해서 물수요량을 예측하는데 있어서 효과적인 예측인자를 도출하는 것이 우선되어야 할 것으로 판단되었다. 이에, 물수요량 특성을 효과적으로 나타내어 줄 수 있는 예측인자로서 강수량, 최저온도, 최고온도, 평균온도 등을 1차적으로 선정하였다. 이들 예측인자들과 서울시 물수요량과의 상관성을 평가하여 최적의 예측인자 Set과 지체시간 등을 산정하였다. 이렇게 선정된 예측인자와 Bayesian 통계기법 기반의 회귀분석 모형을 구축하여 물수요량을 예측하였다. 본 연구에서 적용하고자 하는 계층적 Bayesian 모형은 유사한 특성을 가지는 자료계열들 사이에서 서로 보완이 될 수 있는 정보들을 추출함으로써 모형이 갖는 불확실성을 상당히 줄일 수 있는 방법이다. 이러한 모형적 특징은 생산량 예측에 대한 불확실성 저감 측면에서 장점이 있을 것으로 판단된다. 본 연구에서는 광암, 암사, 구의, 뚝도, 영등포, 강북 정수장을 대상으로 모형의 적합성을 평가하였다. 이러한 연구결과는 향후 정수장 운영계획 및 동일한 시스템을 갖는 상수도 급수량 예측 시 유용하게 사용할 수 있을 것이다.

• Journal of Korea Water Resources Association
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• v.53 no.5
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• pp.383-393
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• 2020

The main objective of this study is to provide a robust model for estimating parameters of the Clark unit hydrograph (UH) using the observed rainfall-runoff data in the Soyangang dam basin. In general, HEC-1 and HEC-HMS models, developed by the Hydrologic Engineering Center, have been widely used to optimize the parameters in Korea. However, these models are heavily reliant on the objective function and sample size during the optimization process. Moreover, the optimization process is carried out on the basis of single rainfall-runoff data, and the process is repeated for other events. Their averaged values over different parameter sets are usually used for practical purposes, leading to difficulties in the accurate simulation of discharge. In this sense, this paper proposed a hierarchical Bayesian model for estimating parameters of the Clark UH model. The proposed model clearly showed better performance in terms of Bayesian inference criterion (BIC). Furthermore, the result of this study reveals that the proposed model can also be applied to different hydrologic fields such as dam design and design flood estimation, including parameter estimation for the probable maximum flood (PMF).

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.975-989
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• 2014

Lasso (Tibshirani, 1996) and Elastic Net (Zou and Hastie, 2005) have been widely used in various fields for simultaneous variable selection and coefficient estimation. Bayesian methods using a conditional Laplace and a double Pareto prior specification have been discussed in the form of hierarchical specification. Full conditional posterior distributions with each priors have been derived. We compare the performance of Bayesian lassos with Laplace prior and the performance with double Pareto prior using simulations. We also apply the proposed Bayesian hierarchical models to real data sets to predict the collapse of governments in Asia.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.747-757
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• 2021

Quantification of extreme rainfall is very important in establishing a flood protection plan, and a general measure of extreme rainfall is expressed as an T-year return level. In this study, a method was proposed for quantifying spatial distribution and uncertainty of daily rainfall depths with various return periods using a hierarchical Bayesian model combined with climate and geographical information, and was applied to the Seoul-Incheon-Gyeonggi region. The annual maximum daily rainfall depth of six automated synoptic observing system weather stations of the Korea Meteorological Administration in the study area was fitted to the generalized extreme value distribution. The applicability and reliability of the proposed method were investigated by comparing daily rainfall quantiles for various return levels derived from the at-site frequency analysis and the regional frequency analysis based on the index flood method. The uncertainty of the regional frequency analysis based on the index flood method was found to be the greatest at all stations and all return levels, and it was confirmed that the reliability of the regional frequency analysis based on the hierarchical Bayesian model was the highest. The proposed method can be used to generate the rainfall quantile maps for various return levels in the Seoul-Incheon-Gyeonggi region and other regions with similar spatial sizes.

• Journal of KIISE
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• v.44 no.10
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• pp.1026-1033
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• 2017

In this paper a Bayesian modeling and duration-based prediction method is proposed for health clinic time series data using the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). HDP-HMM is a Bayesian extension of HMM which can find the optimal number of health states, a number which is highly uncertain and even difficult to estimate under the context of health dynamics. Test results of HDP-HMM using simulated data and real health clinic data have shown interesting modeling behaviors and promising prediction performance over the span of up to five years. The future of health change is uncertain and its prediction is inherently difficult, but experimental results on health clinic data suggests that practical long-term prediction is possible and can be made useful if we present multiple hypotheses given dynamic contexts as defined by HMM states.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.29-37
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• 2017

It has gradually become important to estimate a forest stand volume utilizing LiDAR data. Recently, various statistical models including a linear regression model has been introduced to estimate a forest stand volume using LiDAR data. One of limitations of the current approaches is in that the accuracy of observed forest stand volume data, which is used as a response variable, is questionable unstable. To overcome this limitation, we consider a spatial structure for a forest stand volume. In this research, we propose a hierarchical model for applying a spatial structure to a forest stand volume. The proposed model is applied to the LiDAR data and the forest stand volume for Bonghwa, Gyeongsangbuk-do.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.451-451
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• 2015

최근 1900년부터 1990년 사이 해수면은 매년 평균 1.2mm 상승했지만 1990년부터는 매년 평균 3mm씩 높아지고 있으며, 이에 1990년부터 현재까지 해수면 수위의 상승속도가 이전 90년 동안 측정된 수치보다 2.5배 빠르다는 연구결과가 발표되었다. 해수면 상승으로 인한 피해는 범람과 침식을 야기할 수 있으며 해일 및 폭풍으로 인한 피해를 증가시킴으로 물질적 피해와 인명 피해를 유발할 수 있다. 이러한 이유로 해수면 상승에 따른 과학적인 분석과 신뢰성 있는 전망을 통하여 해수면 상승에 따른 대응과 대비가 필요하다. 이에 본 연구에서는 비정상성 빈도해석 방법을 통하여 미래의 해수면 상승을 고려할 수 있는 비정상성 빈도해석 기법을 개발하였다. 본 연구에서는 극치사상을 추출하기 위해 국립해양조사원 (Korea Hydrographic and Oceanographic Administration, KHOA)에서 관리한 45개 조위관측소의 시 조위 자료를 이용하였다. 45개 조위관측소의 한 시간 단위 자료로부터 연최대 및 연평균 조위계열 (annual average and annual maximum sea level series)을 추출하였다. 본 연구에서는 한반도 해안을 동해안, 서해안, 남해안, 제주 권역으로 구분하고 빈도 해석의 신뢰성을 만족하기 위해 자료 구축기간이 20년 이상이며, 각 해안을 나타낼 수 있는 지점을 선정하였다. 비정상성 빈도해석은 Gumbel 극치분포를 적용하였으며, 계층적 Bayesian 기법을 결합하여 매개변수들에 대한 사후분포를 추정하였다. 본 연구에서는 대부분의 지점에서 비정상성 빈도해석 결과와 정상성 빈도해석 결과와 상당한 차이를 보여주고 있으며, 이는 주로 정상성 가정에 기인하는 문제점으로 판단된다. 향후 기후변화에 따른 연안지역의 홍수 및 사회기반시설의 위험도를 평가하기 위해서는 비정상성을 고려한 빈도해석 절차의 수립과 적용이 필요할 것으로 판단된다.

• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.77-87
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• 2009

Bayesian methods have been focused in recent years for solving small area estimation problems. In this paper, the hierarchical Bayes procedure is implemented via MCMC techniques and compared with the results of One-way, GLM-Normal, and GLM-Gamma cases by analyzing real data of insurance benefit for customer segmentations. After analyzing insurance benefit real data for customer segmentations, we can conclude that the insurance benefit estimator through the small area estimation is more efficient than the estimators by other methods. In addition, we found that the small area estimation gave accurate estimation result for the small number domains.