• Title/Summary/Keyword: 저류함수모형

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Assessment of Parametric Uncertainty for Storage Function Method (저류함수모형 매개변수의 불확실성 평가)

  • Lee Dong-Hee;Kim Jin-Hoon;Bae Deg-Hyo
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.90-94
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    • 2005
  • 본 연구의 목적은 현 국내 홍수 예경보시스템에 사용되고 있는 저류함수모형의 매개변수 변화에 따른 유량해석의 불확실성을 평가하는데 있다. 적용 대상지점으로는 개진을 출구로 하는 낙동강 회천유역($747.5km^2$)이며 비교적 양호한 수문자료를 가지고 있는 단일 호우사상을 선정하였다. 불확실성 평가에 사용된 매개변수는 관측수문곡선을 비교적 정확하게 모의하는 경험적인 값을 기준으로 Monte Carlo 기법을 이용하여 각각의 매개변수(K, P, $T_{1},\;f_{l},\;R_{sa}$)에 대한 정규분포를 가지는 100개의 난수를 발생시켜 저류함수 모형으로 모의되는 유량 앙상블을 평가하였다. 최적화된 매개변수를 이용하여 유량해석을 실시한 결과, 각각의 매개변수가 유출해석에 미치는 영향을 파악할 수 있었으며, 그 중 저류상수 K와 포화누가우량 Rsa가 불확실성이 제일 큰 매개변수로 나타났고, 이들의 혼합 앙상블 유출결과도 매우 큰 불확실성을 내포하는 것으로 나타났다.

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A Study on the Development of the Stochastic Continuous Storage Function Model (추계학적 연속형 저류함수 모형 개발에 관한 연구)

  • Lee, Byong-Ju;Bae, Deg-Hyo
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.231-235
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    • 2009
  • 본 연구에서는 홍수예보를 위한 사상형 모형인 저류함수모형 적용시 문제점을 개선하기 위해 기존의 저류함수 모형에 자유수와 장력수의 2개 영역으로 구성된 토양수분모의 컴포넌트를 결합하여 지표유출, 중간유출, 기저유출의 유출수문성분에 대한 연속적인 모의가 가능하도록 하였으며 실시간 홍수예측을 위해 다수의 유량 관측지점과의 실시간 오차 보정이 가능하도록 앙상블 칼만 필터링 기법을 도입하였다. 개발된 모형의 적용성을 평가하기 위해 낙동강 권역을 대상유역으로 선정하였으며 시단위 강우자료, 기상자료, 유량자료를 비롯하여 GIS를 기반의 지형자료를 구축하였다. 연속형 저류함수형의 매개변수 추정결과 주요지점의 관측유량에 대해 높은 적합도를 보였으며 1시간 선행시간의 홍수량 예측결과에서도 높은 정확도를 보이는 것으로 나타났다.

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Parameter Optimization of Long and Short Term Runoff Models Using Genetic Algorithm (유전자 알고리즘을 이용한 장${\cdot}$단기 유출모형의 매개변수 최적화)

  • Kim, Sun Joo;Jee, Yong Geun;Kim, Phil Shik
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.1117-1121
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    • 2004
  • 본 인구는 유역 물관리에 내한 기초연구로서 유전자 알고리즘(Genetic algorithm)을 사용하여 유역내 장기유출 및 단기유출모형의 매개변수를 최적화하므로 유역의 이${\cdot}$치수관리를 위한 과학적인 유출량산정에 목적이 있다. 장기유출모형은 수정 TANK모형, 단기유출모형은 저류함수모형을 선정하여 최적화를 실시하였다. 또한, 장기유출모형의 홍수기에 대한 부정확성을 보정하기 위해 평수기와 홍수기로 매개변수의 최적화를 실시하므로 수정 TANK모형의 단점을 보완하였다. 수정 TANK모형과 저류함수모형의 적용결과 각각 장${\cdot}$단기 유출량에서 실측값과 비교하여 유의성을 나타냈으며, 홍수시 수정 TANK모형과 실측유출량의 비교결과 최적화 전의 모의 보다 높은 상관성을 나타내므로 본 인구의 수정 TANK모형을 사용하여 유역의 효율적인 장기물수지분석이 가능하리라 판단된다.

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Integrated Storage Function Model with Fuzzy Control for Flood Forecasting (I) - Theory and Proposal of Model - (홍수예보를 위한 통합저류함수모형의 퍼지제어 (I) - 이론 및 모형의 수립 -)

  • Lee, Jeong-Gyu;Kim, Han-Seop
    • Journal of Korea Water Resources Association
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    • v.33 no.6
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    • pp.689-699
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    • 2000
  • This paper presents the integrated storage function model (ISFM) to improve the accuracy of the storage function model (SFM) which is widely employed for flood runoff analysis and its forecasting in Korea. In order to achieve this objective, the optimization method is applied for estimation of parameters of the model which dominate the accuracy of the analysis, which is usually taken by empirical formulae, and they are treated as time dependent variables. The fuzzy control technique is used to detennine the time variant parameters. In addition, the ISFM can be applied to the combined routing of the watershed and the channel with a residual watershed.ershed.

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Parameter Calibration of Storage Function Model and Flood Forecasting (2) Comparative Study on the Flood Forecasting Methods (저류함수모형의 매개변수 보정과 홍수예측 (2) 홍수예측방법의 비교 연구)

  • Kim, Bum Jun;Song, Jae Hyun;Kim, Hung Soo;Hong, Il Pyo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.1B
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    • pp.39-50
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    • 2006
  • The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.

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Stochastic Continuous Storage Function Model with Ensemble Kalman Filtering (II) : Application and Verification (앙상블 칼만필터를 연계한 추계학적 연속형 저류함수모형 (II) : - 적용 및 검증 -)

  • Lee, Byong-Ju;Bae, Deg-Hyo;Shamir, Eylon
    • Journal of Korea Water Resources Association
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    • v.42 no.11
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    • pp.963-972
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    • 2009
  • The objective of this study is to evaluate an application of stochastic continuous storage function model with ensemble Kalman filter technique. The case study is performed at the upstream basin of Jibo streamflow gauge including Andong and Imha dam. Test period is for the rainy season during 2006 and 2007. Long term runoff analysis is feasible in the case of using deterministic model. Ensemble members for input data and parameters are generated using Monte Carlo simulation for the purpose of applying ensemble Kalman filter technique. The cumulative absolute errors of stochastic model to the deterministic one are improved for the amount of 17.5 %, 18.3 % and more than 40.0 % for Andong dam, Imha dam and Jibo station, respectively. The results indicate that the stochastic model improves the accuracy of the simulated discharge considerably.

Improved Parameter Computation Method Applications of Storage Function Model for the Han River Basin (저류함수모형 매개변수 산정 개선방법의 한강유역 적용)

  • Jeong, Dong-Kug;Jeon, Yong-Woon;Lee, Beum-Hee
    • Journal of the Korean Society of Hazard Mitigation
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    • v.8 no.2
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    • pp.149-158
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    • 2008
  • The parameters of each basin, required for the accurate analysis of flood runoff using Storage Function Model, are estimated. Prior to the estimation, sensitivity analysis and extraction of new regional topographic factors for Han River basin are conducted. Based on the result, the outflow constant of basin model is calculated through regression analysis in relation with pre-flood runoff depth. The storage constant of basin model is derived by the optimum storage constant equation, according to the flood event of each basin. The model using the mentioned parameters was compared with K-Water model of Korea Water Resources Corporation and the model of Han River Flood Control Office, and proved to correspond to the observed hydrograph more.

A Study on the Introduction of Fuzzy Theory to the Adjustment of Time-Variant Parameter of Storage Function Method (저류함수법의 시변성 매개변수 조정에 퍼지이론 도입에 관한 연구)

  • 이정규;이창해
    • Water for future
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    • v.29 no.4
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    • pp.149-160
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    • 1996
  • The parameters of the storage function model (SFM) are taken as constants, while they have different values every rainfall events and time of the runoff. Therefore, the results of the SFM show remarkably large errors in general. In this study, the modified sorage function model (MSFM), in which the time variant parameters are introduced, is proposed to improve the SFM which is a conceptual rainfall-runoff model. The fuzzy reasoning is applied as a real-time control method of the time-variant parameters of the proposed model. The applicability of the MSFM was examined in the Bochung river, a tributary of Geum river in Korea. The pattern of predicted outflow hydrograph and peak outflow by the MSFM with fuzzy control are much similar to the measured values in comparison with the results produced by the SFM.

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Design Flood Estimation using Historical Rainfall Events and Storage Function Model in Large River Basins (과거강우사상과 저류함수모형을 이용한 대유역 계획홍수량 추정)

  • Youn, Jong-Woo;Lee, Dong-Ryul;Ahn, Won-Sik;Rim, Hae-Wook
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.3B
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    • pp.269-279
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    • 2009
  • The design flood estimation in a large river basin has a lot of uncertainties in areal reduction factors, time-spatial rainfall distribution, and parameters of rainfall-runoff model. The use of historical concurrent rainfall events for estimating design flood would reduce the uncertainties. This study presents a procedure for estimating design floods using historical rainfall events and storage function model. The design rainfall and time-spatial distribution were determined through analyzing concurrent rainfall events, and the design floods were estimated using storage function model with a non-linear hydrology response. To evaluate the applicability of the procedure of this study, the estimated floods were compared to results of frequency analysis of flood data. Both floods gave very similar results. It shows the applicability of the procedure presented in this study for estimating design floods in practices.

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