• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.769-777
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• 2021

This study investigated the change of the achievement rate of the target water quality conditioned on the occurrence of severe drought, to assess the effects of meteorological drought on the water quality management in the Nakdong River basin. Using three drought indices with difference time scales such as 30-, 60-, 90-day, i.e., SPI30, SPI60, SPI90, and three water quality indicators such as biochemical oxygen demand (BOD), total organic carbon (TOC), and total phosphorus (T-P), we first analyzed the relationship between severe drought occurrence water quality change in mid-sized watersheds, and identified the watersheds in which water quality was highly affected by severe drought. The Bayesian network models were constructed for the watersheds to probabilistically assess the relationship between severe drought and water quality management. Among 22 mid-sized watersheds in the Nakdong River basin, four watersheds, such as #2005, #2018, #2021, and #2022, had high environmental vulnerability to severe drought. In addition, severe drought affected spring and fall water quality in the watershed #2021, summer water quality in the #2005, and winter water quality in the #2022. The causal relationship between drought and water quality management is usufaul in proactive drought management.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.333-343
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• 2022

For water resource management, the design flood is calculated using the flood frequency analysis technique and the rainfall runoff model. The method by design flood frequency analysis calculates the stochastic design flood by directly analyzing the actual discharge data and is theoretically evaluated as the most accurate method. Actual discharge data frequency analysis of the measured flow was limited due to data limitations in the existing flood flow analysis. In this study, design flood frequency analysis was performed using the measured flow data stably secured through the water level-discharge relationship curve formula. For the frequency analysis of design flood, the parameters were calculated by applying the bayesian inference, and the uncertainty of flood volume by frequency was quantified. It was confirmed that the result of calculating the design flood was close to that calculated by the rainfall-runoff model by applying long-term rainfall data. It is judged that hydrological analysis can be done from various perspectives by using long-term actual flow data through hydrological survey.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.377-390
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• 2017

When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

• Journal of Korea Water Resources Association
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• v.46 no.1
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• pp.59-71
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• 2013

The lack of sufficient flood data being kept across Korea has made it difficult to assess reliable estimates of the design flood while relatively sufficient rainfall data are available. In this regard, a rainfall simulation based derivation technique of flood frequency curve has been proposed in some of studies. The main issues in deriving the flood frequency curve is to develop the rainfall simulation model that is able to effectively reproduce extreme rainfall. Also the rainfall-runoff modeling that can convey uncertainties associated with model parameters needs to be developed. This study proposes a systematic approach to fully consider rainfallrunoff related uncertainties by coupling a piecewise Kernel-Pareto based multisite daily rainfall generation model and Bayesian HEC-1 model. The proposed model was applied to generate runoff ensemble at Daechung Dam watershed, and the flood frequency curve was successfully derived. It was confirmed that the proposed model is very promising in estimating design floods given a rigorous comparison with existing approaches.

• KDI Journal of Economic Policy
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• v.36 no.2
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• pp.103-136
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• 2014

The covered interest rate parity condition (CIRP) has been widely used in open macroeconomic analysis, risk management, exchange rate forecasts, and so forth. Due to the recent global financial crises, there have been remarkable changes in the financial markets of the emerging markets. These changes possibly influenced the dynamics of the covered interest rate parity condition. In this paper, we investigate whether the CIRP dynamics has changed, and what is the nature of the regime changes. To do this, we propose and estimate multiple-state Markov regime switching models using a Bayesian MCMC method. Our estimation results indicate that the default risk or the deviation from the CIRP has been decreased after the crisis. It seems to be associated with the more active interaction between the short-term bond market and the short-term foreign exchange market than before. The tightened relation of these two financial markets is caused by the arbitrage transaction of foreign investors.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.177-184
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• 2010

In these day, many data including sensor, delivery, credit and stock data are generated continuously in massive quantity. It is difficult to learn from these data because they are large in volume and changing fast in their concepts. To handle these problems, learning methods based in sliding window methods over time have been used. But these approaches have a problem of rebuilding models every time new data arrive, which requires a lot of time and cost. Therefore we need very simple incremental learning methods. Bayesian method is an example of these methods but it has a disadvantage which it requries the prior knowledge(probabiltiy) of data. In this study, we propose a learning method based on attribute values. In the proposed method, even though we don't know the prior knowledge(probability) of data, we can apply our new method to data. The main concept of this method is that each attribute value is regarded as an expert learner, summing up the expert learners lead to better results. Experimental results show our learning method learns from data very fast and performs well when compared to current learning methods(decision tree and bayesian).

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.462-462
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• 2017

유량은 기후적 요인과 인간활동 요인에 의하여 변동된다. 특히 우리나라는 지난 30년 동안 전지구적인 기후변화와 특정지역에서의 인간활동의 변화가 급격하게 진행된 바 있으므로, 합리적인 수자원 계획을 수립하기 위해서는 두 가지 요소들로 인한 유량의 변동량을 정량적으로 분리하여 분석할 필요가 있다. 또한 우리나라와 같이 연강수량의 대부분이 특정 계절에 집중되는 국가나 유역에서는 월별, 계절별 및 년별로 구분된 수문분석을 시행하여야 보다 실질적인 수자원 관리계획을 수립할 수 있다. 그러나 유량의 변동량을 특정 원인별로 구분하여 분석하고자 하는 연구는 기존의 홍수나 가뭄 자체에 관한 연구에 비해 미미한 형편이며, 다양한 시간단위를 이용한 원인별 유량 변동량의 산정에 관한 연구는 더욱 찾아보기 힘들다. 따라서 본 연구에서는 기후변화로 및 인간활동으로 인한 유량 변동량을 정량적으로 분리하기 위하여 수문모형(hydrological model)을 이용한 방법과 수문학적 민감도 (hydrological sensitivity) 분석 방법을 소양강 상류유역 및 섬강 유역에 대해 적용하고 유량 변동량의 결과를 월별, 분기별 및 년별로 구분하여 제시하였다. 먼저 두 유역에 대한 기후변화 및 인간활동의 양상을 강수, 온도, 유량, 인구변화, 불투수층 변화의 추세를 통해 파악하였으며, 인간활동으로 인해 발생되는 급진적인 변동점을 탐색하기 위해 이중누가곡선, Pettitt 검정 및 베이지안 변동점 (Bayesian change point) 분석을 시행하였다. 탐색된 변동점을 활용하여 수문모형에 의한 유량 변동량을 정량화하기 위하여 변동점 이전 구간에 대해 보정 및 검증된 SWAT모형을 사용하였으며, 6가지의 Budyko 곡선 함수들로부터 각각 유량 변동량을 산정하여 수문모형에 의한 유량 변동량을 검증하였다. 최종적으로 수문모형을 이용한 방법을 통해 두 유역에 대한 기후변화 및 인간활동으로 인한 유량 변동량을 정량화하였다. 소양강 상류유역은 기후변화로 인한 유량 변동량이 인간활동으로 인한 유량변동량보다 상대적으로 크게 산정되었으며, 섬강 유역은 소양강 유역과 반대의 결과를 보이는 것으로 분석되었다. 특히 본 연구에서는 해당 분석결과를 월별 및 분기별로 구분하여 제시함으로써, 향후 특정 지역 및 시기에서의 합리적인 수자원 관리계획의 수립에 활용될 수 있는 기초적인 자료를 제공하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.79-79
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• 2021

기상학적 측면에서 강수 부족으로 인한 수생태환경(하천), 호소환경(저수지) 및 유역환경(중권역)으로 미치는 환경학적 가뭄의 영향을 평가하기 위한 시도는 매우 중요하다. 만약 동일한 규모의 강수부족 현상이 발생할지라도, 환경적 측면에서의 수질 및 수생태에 미치는 영향이 매우 큰 유역이 있고, 반면 어느 정도의 복원력을 유지할 수 있는 유역이 있을 것이다. 즉, 서로 다른 유역환경에 따라 가뭄으로 인한 환경적 영향은 달라질 가능성이 크며, 이처럼 환경적 가뭄에 취약한 지역을 위해서는 지속적인 환경가뭄 모니터링이 중요하다. 환경적 측면에서 가뭄의 영향을 평가하기 위해서는 다양한 수질 관련 항목을 연계한 환경가뭄 감시가 중요하며, 이와 더불어 가뭄과 관련한 다양한 이해관계자 간의 효율적인 의사결정 도구가 필요하다. 따라서 본 연구에서는 다양한 시나리오 정보를 제공할 수 있는 베이지안 네트워크 모형을 적용하여 환경가뭄 민감도 평가 방안을 제시하고자 한다. 본 모형에서는 수질 문제가 가장 심하게 대두되고 있는 낙동강 유역을 대상으로, 기상학적 가뭄에 의한 수생태 및 환경 관련 변수들(BOD, T-P, TOC)의 복잡한 상호의존성을 파악할 수 있는 베이지안 네트워크 모형을 활용하였다. 또한, 기상학적 가뭄에 의한 상류와 하류 간의 환경적 영향을 연계하여 해석하기 위한 모형을 구축하였다. 그 결과, 기상학적 가뭄으로 인한 환경적 민감도가 크게 나타나는 중권역(예: 임하댐유역)과 이와 반대인 중권역(예: 병성천유역)의 구분이 가능하였다. 또한, 상류에서 발생한 심한 기상학적 가뭄이 하류 지역 내 환경적인 영향을 지속할 가능성이 있음을 확인되었다. 따라서 본 연구에서 제안한 방법은 환경적 가뭄의 취약지역을 우선 선정하고, 나아가 상-하류 간의 환경적 가뭄을 감시하는 데 있어 활용도가 있을 것으로 기대된다.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.373-384
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• 2014

Data mining is to clarify pattern or correlation of mass data of complicated structure and to predict the diverse outcomes. This technique is used in the fields of finance, telecommunication, circulation, medicine and so on. In this paper, we selected risk factors of respiratory diseases in the field of medicine. The data we used was divided into respiratory diseases group and health group from the Gyeongsangbuk-do database of Community Health Survey conducted in 2012. In order to select major risk factors, we applied data mining techniques such as neural network, logistic regression, Bayesian network, C5.0 and CART. We divided total data into training and testing data, and applied model which was designed by training data to testing data. By the comparison of prediction accuracy, CART was identified as best model. Depression, smoking and stress were proved as the major risk factors of respiratory disease.