• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.154-158
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• 2008

현재 홍수예경보에서는 강우-유출 모형, 통계학적 모형 등 다양한 모형을 사용하고 있으며, 우리나라에서는 특히 저류함수모형을 사용하여 홍수예보를 수행하고 있다. 이에 본 연구에서는 저류 함수모형과 Tank 모형, SSARR 모형을 이용해 금강유역의 미호천 유역, 일본의 Kusaki 댐 유역, 베트남의 Ta Trach 유역에 대하여 홍수모의예측을 수행하였다. 강우-유출 모형인 저류함수모형과 Tank 모형, SSARR 모형의 경우 매개변수 보정에 대한 연구를 추가하여 홍수예측 모형의 효율성의 증대를 도모 하였다. 매개변수 보정을 위하여 유전자 알고리즘, Pattern Search multi-start, SCE-UA등의 최적화 기법들을 이용하였고, 목적함수로는 WSSR과 SSR를 적용하여 그 결과를 비교, 분석하였다.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.523-526
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• 2003

This study is to test the applicability of SLURP (Semi-distributed Land Use-based runoff Process) model that is a semi-distributed, continuous hydrologic model developed by Kite (1997). The Soyanggang-dam watershed ($2,694km^2$) was selected. The DEM, land-cover map, monthly NDVI from NOAA/AVHRR and daily meteorological data of 2001 were prepared. By using the parameter optimization technique, SCE-UA (Shuffled Complex Evolution-University of Arizona), the model was calibrated and the Nash-Sutcliffe efficiency was 0.73.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1174-1178
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• 2010

확산-유추를 기반으로 한 지형학적 순간단위도의 유역의 동역학적 매개변수인 특성유속과 확산계수를 실제수문사상과 수치지형도상의 실제 하천망(Blue line)을 이용해 전역최적화 기법인 Shuffled Complex Evolution(SCE-UA)을 적용하여 산정하여 이를 하천망의 해상도에 따른 유역의 수문응답의 특성을 살펴본 결과 동일한 산정된 유속조건하에서 하천망의 해상도가 커짐에 따라 첨두시간과 지체시간은 작아지는 반면 첨두유량은 커지는 현상을 보였으며, 순간단위도의 형상을 결정짓는 유하시간 분포의 분산은 동수역학적 분산에 비해 지형학적 분산에 지배적인 영향을 받음을 알 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.326-326
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• 2018

The study will assess the seasonal effect of hydrological models on performance and parameters for streamflow simulation. TPHM, GR4J, CAT, and TANK-SM hydrological models will be applied for simulating streamflow in ten small and large watersheds located in South Korea. The readily available hydrometeorological data will be applied as an input to the four hydrological models and the potential evapotranspiration will be computed using the Penman-Monteith equation. The SCE-UA algorithm implemented in PEST will be used to calibrate the models considering similar objective functions bedside the calibration will be renewed to capture the seasonal effects on the model performance and parameters. The seasonal effects on the model performance and parameters will be presented after assessing the four hydrologic models results. The conventional approach and season-based results will be evaluated for each model in the tested watersheds and a conclusion will be made based on the finding of the results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2255-2265
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• 2013

This study presents the width function-based Clark model. To this end, rescaled width function with distinction between hillslope and channel velocity is used as time-area curve and then it is routed through linear storage within the framework of not finite difference scheme used in original Clark model but analytical expression of linear storage routing. There are three parameters focused in this study: storage coefficient, hillslope velocity and channel velocity. SCE-UA, one of the popular global optimization methods, is applied to estimate them. The shapes of resulting IUHs from this study are evaluated in terms of the three statistical moments of hydrologic response functions: mean, variance and the third moment about the center of IUH. The correlation coefficients to the three statistical moments simulated in this study against these of observed hydrographs were estimated at 0.995 for the mean, 0.993 for the variance and 0.983 for the third moment about the center of IUH. The shape of resulting IUHs from this study give rise to satisfactory simulation results in terms of the mean and variance. But the third moment about the center of IUH tend to be overestimated. Clark model proposed in this study is superior to the one only taking into account mean and variance of IUH with respect to skewness, peak discharge and peak time of runoff hydrograph. From this result it is confirmed that the method suggested in this study is useful tool to reflect the heterogeneity of drainage path and hydrodynamic parameters. The variation of statistical moments of IUH are mainly influenced by storage coefficient and in turn the effect of channel velocity is greater than the one of hillslope velocity. Therefore storage coefficient and channel velocity are the crucial factors in shaping the form of IUH and should be considered carefully to apply Clark model proposed in this study.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.343-356
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• 2014

The SWMM (storm water management model) has been widely used in the world and is a watershed runoff simulation model used for a single event or a continuous simulation of runoff quantity and quality. However, there are many uncertain parameters in the watershed runoff continuous simulation module and the water quality module, which make it difficult to use the SWMM. The purpose of the study is to develop an automatic calibration module of the SWMM not only for watershed runoff continuous simulation, but also water quality simulation. The automatic calibration module was developed by linking the SWMM with the SCE-UA (shuffled complex evolution-University of Arizona) that is a global optimization algorithm. Estimation parameters of the SWMM were selected and search ranges of them were reasonably configured. The module was validated by calibration and verification of the watershed runoff continuous simulation model and the water quality model for the Donghyang Stage Station Basin. The calibration results for watershed runoff continuous simulation model were excellent and those for water quality simulation model were generally satisfactory. The module could be used in various studies and designs for watershed runoff and water quality analyses.

• Journal of Korea Water Resources Association
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• v.38 no.11
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• pp.937-946
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• 2005

The objective of the study is to develop a reservoir optimal operation model and to suggest the appropriate amount of additional water supply and optimal operation rule. The model uses multiple objective function and a global search method, SCE-UA method. The objective function is set up to maintain the storage at target level, to satisfy the water demand, and to maximize the hydropower product. To evaluate the model's applicability, the model was applied for allocating the optimal water depending on storage level changes of Seomjin dam. The results comparing optimal operation and historical data showed that hydropower product increased from $-2.29\%$ to $14.51\%$, $-5.94\%$ to $3.98\%$, and $-0.43\%$ to $6.35\%$ with varying target levels in wet, dry, and normal period, respectively. Also, The model was applied for assessing water supply capacity of Seomjin dam to satisfy increasing water demand. The dam was operated by the model on consideration of downstream flow as 0.17, 0.50, 0.70, 1.0, 1.5, and $3.0\;m^3/sec$. The results showed that in case of operating the dam with downstream flow less than $0.70\;m^3/sec$ and with target water level lower than 194.0 m, hydropower product was more than the historical operation data and existing amount of water supply was less influenced.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.4
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• pp.9-19
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• 2014

The objective of this study was to evaluate of the Tank model in simulating runoff discharge from rural watershed in comparison to the SWAT (Soil and Water Assessment Tool) model. The model parameters of SWAT was calibrated by the shuffled complex evolution-university Arizona (SCE-UA) method while Tank model was calibrated by genetic algorithm (GA) and validated. Four dam watersheds were selected as the study areas. Hydrological data of the Water Management Information System (WAMIS) and geological data were used as an input data for the model simulation. Runoff data were used for the model calibration and validation. The determination coefficient ($R^2$), root mean square error (RMSE), Nash-Sutcliffe efficiency index (NSE) were used to evaluate the model performances. The result indicated that both SWAT model and Tank model simulated runoff reasonably during calibration and validation period. For annual runoff, the Tank model tended to overestimate, especially for small runoff (< 0.2 mm) whereas SWAT model underestimate runoff as compared to observed data. The statistics indicated that the Tank model simulated runoff more accurately than the SWAT model. Therefore the Tank model could be a good tool for runoff simulation considering its ease of use.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.1
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• pp.1-9
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• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.195-195
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• 2012

인공신경망 모형은 복잡하고 비선형의 입력과 출력 관계를 잘 반영할 수 있어서 유출 모델링에 널리 적용되어 왔다. 그러나 인공신경망 모형은 강우나 유역특성의 공간적 분포를 반영하는 것이 어려우며 물리적 개념이 결여되어 있는 단점이 있다. 본 연구에서는 유역특성과 물리적 개념을 반영할 수 있는 물리기반 모형과 인공신경망 모형의 장점들을 조합하여 물리기반 모형의 일 유출량 해석 능력을 향상하기 위하여 SWAT 모형과 인공신경망(ANN)을 연계하였다. SWAT-ANN 연계모형은 두 단계로 구성되어 진다. 첫 번째 단계에서는 관측 자료를 이용하여 SWAT 모형을 보정한다. 두 번째 단계에서는 첫 번째 단계에서 계산한 소유역별 SWAT 모형의 유출결과를 ANN의 입력자료로 이용하여 SWAT-ANN 연계모형을 구축한다. SCE-UA 최적화 방법을 적용하여 SWAT 모형의 매개변수들을 보정하였고, ANN 학습은 3층의 feed-forward 역전파 알고리즘에 기초한 Bayesian Regularization 방법을 적용하였다. ANN 은닉층의 뉴런 및 전달함수는 시행착오를 통하여 적절한 ANN 구조를 설정하여 SWAT-ANN 연계모형의 일유출량을 모의하였다. 여러 가지 통계적 오차기준을 이용하여 보청천 유역에서 SWAT-ANN 연계모형의 결과와 SWAT 단독 모형의 결과를 비교하였다. SWAT-ANN 연계모형이 SWAT 단독 모형보다 더 우수한 결과를 나타내어 일 유출량 해석을 위한 SWAT-ANN 연계모형의 유용성을 확인할 수 있었다.