• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.359-359
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• 2019

2011년 4대강 다기능보 설치에 따라 배수영향을 받는 구간은 기존의 측정방법으로는 유량측정이 어려워 보 구간별로 자동유량측정시설을 설치하여 실시간으로 유량을 생산하고 있다. 하지만 현재 수질개선 및 하천 자연화를 위해 보 운영수위를 저하시켰다. 이에 수위 저하에 따른 측정영역 한계로 정상적인 운영에 어려움이 발생하고 있다. 본 연구에서는 낙동강 합천창녕보 영향 구간 내 합천군(율지교), 합천군(적포교) 지점을 대상으로 상하류 유량관계를 활용하여 결측 및 오측자료를 보완하고자 한다. 대상지점은 2018년 수문개방에 따른 수위저하로 인한 결측과 부유물에 걸림에 의한 유속자료 오측으로 자료의 보완이 필요하였다. 이로 인해 자료 보완을 위하여 각 지점의 환산유량을 이용하여 경향성 검토를 하였으며, 상하류 환산유량과 검보정 측정성과와의 관계를 활용한 관계식을 개발하여 유량을 산정하였다. 산정된 유량과 검보정 측정결과 상관도(R2)는 0.95 이상으로 나타나 매우 합리적으로 판단되나 수문조작 시에 일부 편차는 보인다는 결과를 나타냈다. 단기적인 자료보완은 품질관리를 통해 다양한 방법으로 가능하지만 상하류 유량관계를 활용한 방법이 장기적인 자료를 보완하는 방법으로 적절하다고 판단된다. 향후 보완방법을 다른 보 구간의 지점에도 적용하여 보 수위저하에 따른 시설물 개선공사가 진행되는 동안 실시간 유량자료의 제공으로 연속적인 유량자료 생산이 가능하도록 하고자 한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.684-689
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• 2006

Rainfall is the most basic input data to analyze the hydrologic system. When we measure the rainfall data, the rainfall data can be missing due to various reasons. Therefore, various interpolation methods are available for compensating the missing data. However, the interpolation methods were used without considering their applicability and accuracy. This study compares the interpolation methods such as the arithmetic mean method, normal ratio method, modified normal ratio method, inverse distance method, linear programming, Kriging method to estimate the existing rainfall correction method.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.264-264
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• 2020

최근 기후변화로 우리나라는 과거에 비해 태풍이나 국지성 집중호우 및 가뭄 등 극심한 수문현상이 빈번하게 발생하고 그 피해가 더욱 커지고 있는 추세이다. 특히 우리나라의 경우 산지가 많으며 대부분의 하천이 유역면적이 작고 유로연장이 짧아 단시간에 유출이 발생하며 수문학적 특성이 연중 큰 편차를 보이고 있다. 이러한 이상기후에 따른 수문현상 파악 및 피해 경감을 위해 신뢰성 있는 수문자료는 매우 중요하다. 따라서 수문자료에 대한 품질관리는 필수적이지만 자료 결측 및 오측에 대한 신뢰성 높은 품질관리가 이뤄지지 못하고 있는 실정이다. 현재 수위자료의 결측이 발생한 경우 해당 관측소의 수위 자료를 사용해 선형보간 및 운형자법으로 수정하거나 상·하류 관측소의 관계를 이용하여 회귀분석을 통해 자료 결측의 수정 및 보완을 수행하는 등 담당자의 주관적 판단에 의존하고 있다. 본 논문에서는 신뢰성 높은 수문자료의 결측치 보완 및 예측을 위한 방안을 제시하고자 상류의 관측소의 수문자료를 이용한 하류의 단시간 수문 자료예측에 관한 연구를 수행하였다. 이를 위해 자료지향형 모델인 적응형 뉴로-퍼지 기법(Adaptive Network-based Fuzzy Inference System, ANFIS)을 이용한 모형을 적용하였다. 기존의 연구에서 가장 일반적으로 사용되는 물리적 모형은 수문자료를 활용하여 수위 및 유출을 산정함에 있어 매개변수의 결정이 어렵고 많은 오차들을 내포하고 있다. 본 연구에서 사용한 ANFIS는 입력자료와 출력자료만을 고려하여 구축할 수 있기 때문에 자료 수집단계에서 유역의 물리적 자료 및 지형 자료와 같은 방대한 양의 자료 수집이 필요가 없다. 이후 모형이 구축이 된다면 입·출력 자료만을 이용하여 신뢰성 높은 결과를 획득할 수 있지만 입력 자료의 품질에 따라 결과가 좌우되기 때문에 자료의 구성이 매우 중요하다. 본 연구에서는 ANFIS를 통해 무주남대천 유역의 무주군(여의교) 관측소의 수위자료를 입력자료를 사용하여 하류에 위치한 무주군(취수장) 관측소의 수문자료의 결측 보완 및 예측하는 모형을 구축하고 모형의 구조 변화를 통해 가장 정확도 높은 모형을 결정하였다.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.220-233
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• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.4
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• pp.382-391
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• 2020

In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.690-696
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• 2006

This study applies the methods proposed in this issue[1] to the 11 rainfall gauging stations of the Pyongchang area. Also, this study analyzes the error range of each interpolation method, and considers spatial distribution according to the number of gauging station. As the results, the linear programming methods shows the best minimum error. However, this method might be difficult to apply in the field because of need for programming. Comparatively, the inverse distance method shows more simple and accurate results than the linear programming one. The result of this study could contribute to the increase of accuracy for the filling of missing rainfall data.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.635-647
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• 2013

Imputation is a commonly used method to handle missing survey data. The performance of the imputation method is influenced by various factors, especially an outlier. The removal of the outlier in a data set is a simple and effective approach to reduce the effect of an outlier. In this paper in order to improve the precision of multiple imputation, we study a imputation method which reduces the effect of outlier using various weight adjustment methods that include the removal of an outlier method. The regression method in PROC/MI in SAS is used for multiple imputation and the obtained final adjusted weight is used as a weight variable to obtain the imputed values. Simulation studies compared the performance of various weight adjustment methods and Monthly Labor Statistic data is used for real data analysis.

• Survey Research
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• v.9 no.2
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• pp.1-27
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• 2008

Problems of incomplete data are pervasive in statistical analysis. In particular, incomplete data have been an important challenge in repeated measures studies. The objective of this study is to give a brief introduction to missing data mechanisms and conventional/recent missing data methods and to assess the performance of various missing data methods under ignorable and non-ignorable missingness mechanisms. Given the inadequate attention to longitudinal studies with missing data, this study applied recent advances in missing data methods to repeated measures models and investigated the performance of various missing data methods, such as FIML (Full Information Maximum Likelihood Estimation) and MICE(Multivariate Imputation by Chained Equations), under MCAR, MAR, and MNAR mechanisms. Overall, the results showed that listwise deletion and mean imputation performed poorly compared to other recommended missing data procedures. The better performance of EM, FIML, and MICE was more noticeable under MAR compared to MCAR. With the non-ignorable missing data, this study showed that missing data methods did not perform well. In particular, this problem was noticeable in slope-related estimates. Therefore, this study suggests that if missing data are suspected to be non-ignorable, developmental research may underestimate true rates of change over the life course. This study also suggests that bias from non-ignorable missing data can be substantially reduced by considering rich information from variables related to missingness.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.