• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.244-244
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• 2023

Both the frequency and the magnitude of hydrometeorological extreme events such as severe floods and droughts are increasing. In order to prevent a damage from the climatic disaster, hydrological models are often simulated under various meteorological conditions. While performing the simulations, a synthetic data generated through time series models which maintains the key statistical characteristics of the sample data are widely applied. However, the synthetic data can easily maintains both the average and the variance of the sample data, but the quantile is not maintained well. In this study, we proposes a data generation method which maintains the quantile of the sample data well. The equations of the former maintenance of variance extension (MOVE) are expanded to maintain quantile rather than the average or the variance of the sample data. The equations are derived and the coefficients are determined based on the characteristics of the sample data that we aim to preserve. Monte Carlo simulation is utilized to assess the performance of the proposed data generation method. A time series data (data length of 500) is regarded as the sample data and selected randomly from the sample data to create the data set (data length of 30) for simulation. Data length of the selected data set is expanded from 30 to 500 by using the proposed method. Then, the average, the variance, and the quantile difference between the sample data, and the expanded data are evaluated with relative root mean square error for each simulation. As a result of the simulation, each equation which is designed to maintain the characteristic of data performs well. Moreover, expanded data can preserve the quantile of sample data more precisely than that those expanded through the conventional time series model.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.855-862
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• 1998

This paper investigates the impact of the forecast error on performance of a reservoir system for hydropower production. Forecast error is measured as th Root Mean Square Error (RMSE) and parametrically varied within a Generalized Maintenance Of Variance Extension (GMOVE) procedure. A set of transition probabilities are calculated as a function of the RMSE of the GMOVE procedure and then incorporated into a Bayesian Stochastic Dynamic Programming model which derives monthly operating policies and assesses their performance. As a case study, the proposed methodology is applied to the Skagit Hydropower System (SHS) in Washington state. The results show that the system performance is a nonlinear function of RMSE and therefor suggested that continued improvements in the current forecast accuracy correspond to gradually greater increase in performance of the SHS.

• Journal of Korean Society on Water Environment
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• v.30 no.1
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• pp.60-67
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• 2014

In this study, four methods for calculation of continuous daily flow was suggested using short-term or partial recording station of streamflow including missing data. Using these methods, standard flows at the outlet of unit/small basins for the management of total maximum daily loads (TMDLs) in Namgang dam basin were estimated from full-period flow duration curve (FDC). Four methods of extension are described, and their properties are explored. The methods are regression (REG), regression plus noise (RPN), and maintenance of variance extension types 1 and 2 (MOVE.1, MOVE.2). In these methods, the continuous daily flow was calculated using extension equation based on correlation analysis, after conducting the correlation analysis between historic record of streamflow and long-term recording station (a base station). Finally the best optimal method was selected as the MOVE.2, and the standard flows in the abundant, ordinary, low and drought flow estimated from FDC was evaluated using MOVE.2 in unit/small basins.

• Journal of Korean Society on Water Environment
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• v.33 no.1
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• pp.51-62
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• 2017

The purpose of this study is to evaluate on the applicability of Load Duration Curve (LDC) method using Maintenance of Variance Extension types 2 method and sampling data for efficient total maximum daily loads at the Nakbon-A unit watershed in Korea. The LDC method allows for characterizing water quality data such as BOD, TOC, T-N and T-P in this study at different flow regimes(or quarters). BOD usually exceeded the standard value (exceedance probability 50%) at low flow zone. On the other hand, TOC, T-N, T-P usually exceeded the standard value at dry and low flow zone. Seasonally all water quality variables usually exceeded the standard value at Q1(Jan-Mar) and Q2(Apr-Jun) zones. Improvement of effluent control from wastewater treatment plants are effective to improve BOD and T-P.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.397-397
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• 2011

2004년부터 4대강 물환경연구소는 수질오염총량관리제의 원활한 추진을 위해 총량관리단위유역 말단부에서 8일 간격으로 청천(晴天)시를 중심으로 유량과 수질을 동시에 측정하기 시작하였다. 그 결과 연중 하천유량과 수질의 연동 여부 및 변동 추이를 확인하는 것이 가능하게 되었다. 그러나 8일 간격으로 생산되는 유량은 지침의 정의와 맞물려 기준유량의 산정에 또 다른 어려움을 주고 있다. '한강수계 오염총량관리계획수립 지침'에 따르면 '기준유량은 과거 10년간 평균 저수량으로 한다'고 명시되어 있다. 여기서 저수량이란 유량의 크기를 누가일수로서 표시하여 1년을 통하여 275일은 이보다 더 작지 않은 유량으로 정의된다. 따라서 정확한 저수량을 산정하기 위해서는 1년 365개 매일의 유량자료가 필요하다. 하지만 8일 간격으로 유량을 측정하게 되면 1년 365개 대신 최대 45 여개의 일 유량자료만 취득 가능하므로 유황분석에 어려움이 발생할 수밖에 없다. 본 연구에서는 수질오염총량관리단위유역의 말단부에서 8일 간격으로 계측된 유량자료가 있을 때 이를 연속적인 일유량으로 확대할 수 있는 방법론 중 하나를 소개한다. 미 지질조사국(USGS)에서 주로 사용되는 이 방법은 A지점(부분계측이 이루어지는 지점)의 결측치를 동일 유역 혹은 수문학적으로 유사한 유역의 B지점(연속계측이 이루어지는 지점)의 자료를 이용하여 보완하는 방식이다. 이를 위해 먼저 부분계측이 이루어진 날과 같은 날짜의 유량자료를 연속계측자료에서 추출한 다음 두 자료(A지점에서의 모든 유량과 B지점에서의 추출된 유량)의 상관성을 비교해 본다. 두 자료간에 상관도가 높다면 이를 잘 표현하는 방정식을 통해 A지점의 결측치를 내 외삽한다. 여기서 두 자료간 상관도를 잘 묘사할 수 있는 방법으로 본 연구에서는 최소제곱법(Least Square Estimator, LSE)과 분산확장법(Maintenance of Variance Extension, MOVE)을 비교,분석해 보았다. 한강수계 수질오염총량관리단위유역 중 동일지점에 8일 간격 부분계측 유량자료와 일 연속자료가 동시에 존재하는 곳이 6지점이 있었으며 이 자료들을 바탕으로 LSE와 MOVE의 정확도를 검증해 본 결과 MOVE가 일 연속유량 확장에 더 나은 결과를 보였다.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.213-231
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• 2021

Purpose: This study intends to provide decision-making information to improve efficiency by analyzing the management efficiency of smart greenhouse business entities and identifying factors that affect the efficiency based on input and output. Methods: The subjects of analysis were business entities for cultivating strawberries in smart greenhouses in Jeolla region (northern and southern Jeolla provinces), and the analysis focused on the management performance of the 2019-2020 crop period (year). Data Envelopment Analysis(DEA) was applied as an analysis method for efficiency analysis, Quantile Regression(QR) analysis was applied as a factor affecting the efficiency. Results: The reason for the efficiency gap between business entities was that there were many business entities that did not minimize the input cost at the current level of output, and the area where the variance among business entities was large was the fixed cost per 10a. In the results of the affecting factor analysis, it was found that the seed-seedlings cost, fertilizer cost, other material cost, and employment and labor cost had a negative (-) effect on the efficiency, and that the repair and maintenance cost had a positive (+) effect. Conclusion: Therefore, to achieve the efficiency of scale, it is necessary to reduce the input scale to an appropriate level. In the case of business entities with low efficiency by quartile, the seed-seedlings, fertilizer, and other material costs reduce expenditures, and repair maintenance costs can improve efficiency by increasing expenditures.

• Journal of Korea Water Resources Association
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• v.45 no.1
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• pp.91-99
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• 2012

In this study, it was proposed that extension techniques of 8 day interval recorded stream-flow data which has been produced at the mouth of unit watersheds for TMDLs to daily one. The concept of this method was that the missing data at partial recording station was filled by using the daily data at a nearby, hydrologically similar streamgaging station. First, same day stream-flow was extracted from the daily stream-flow. Then, the extension equation was developed based on the sample data when the same day stream-flow from daily data and the partially recorded stream-flow was deeply related each other. The missing data was interpolated or extrapolated by the equation. Especially the maintenance of variance extension (MOVE) technique was used to derive the equation and was validated. Finally the 8-day interval recorded stream-flow at the mouth of unit watersheds in Han River Basin for TMDLs was extended to continuously daily data by using the method proposed in this study. And the low flow at each unit watershed was evaluated according to the flow-duration curve.