• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.74-82
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• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.722-725
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• 2007

Geum River Estuary Barrage is very important for the irrigation, municipal and industrial uses in the cities of Gunsan, Iksan and Jeonju. The Geum River Control Office has a flood forecasting system; however, the current system does not consider the backwater effects. As a result, it is very difficult to give correct flood information, and it is difficult to accurately assess the water resource supply and saltwater invasion into freshwater, as frequently occurs due to over-discharge during floods. In this study, we investigate the flood forecasting system for the Geum River reach influenced by the estuary barrage. The current system cannot consider the backwater effect because the estuary barrage blocks the end of the river. We calculated the discharge from the tide lock and evaluated the inside water level of the estuary barrage during floods. The results show that the calculation agrees well with the observed data at the river stage stations in the Geum River. The results also show that this program is a reasonable substitute for the current system.

• Journal of Korea Water Resources Association
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• v.37 no.9
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• pp.771-780
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• 2004

The purpose of this study is to test a development of system for flood runoff forecasting using neural network model. As the forecasting models for flood runoff the neural network model was tested with the observed flood data at Gongju and Buyeo stations. The neural network model consists of input layer, hidden layer, and output layer. For the flood events tested rainfall and runoff data were the input to the input layer and the flood runoff data were used in the output layer. To make a choice the forecasting model which would make up of runoff forecasting system properly, real-time runoff of river when flood periods were forecasted by using neural network model and state-space model. A comparison of the results obtained by the two forecasting models indicated the superiority and reliability of the neural network model over the state-space model. The neural network model was modified to work in the Web and developed to be the basic model of the forecasting system for the flood runoff. The neural network model developed to be used in the Web was loaded into the server and was applied to the main stream of Geum river. For the main stage gauging stations mentioned above the applicability of the selected forecasting model, the Neural Network Model, was verified in the Web.

• Journal of Korean Society on Water Environment
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• v.33 no.2
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• pp.219-229
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• 2017

A watershed-river linked modeling system was developed to forecast the water quality, particularly weekly changes in chlorophyll-a concentration, of the Yeongsan River, Korea. Hydrological Simulation Program-Fortran (HSPF) and Environmental Fluid Dynamics Code (EFDC) were adopted as the basic model framework. In this study, the EFDC model was modified to effectively simulate the operational condition and flow of multi-functional weirs constructed in the main channel of rivers. The model was tested against hydrologic, water quality and algal data collected at the right upstream sites of two weirs in 2014. The mean absolute errors (MAEs) of the model calibration on the annual variations of river stage, TN, TP, and algal concentration are 0.03 ~ 0.10 m, 0.65 ~ 0.67 mg/L, 0.03 ~ 0.04 mg/L, and $9.7{\sim}10.8mg/m^3$, respectively. On the other hand, the MAE values of forecasting results for chlorophyll-a level at the same sites in 2015 range from 18.7 to $22.4mg/m^3$, which are higher than those of model calibration. The increased errors in forecasting are mainly attributed to the higher uncertainties of weather forecasting data compared to the observed data used in model calibration.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.709-712
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• 2004

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. h short-term water demand forecasting technology will be developed fatting into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-time Water Information System. The well-known SSARR model was selected for the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.606-609
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• 2015

본 연구에서는 support vector regression (SVR) 및 매개변수 최적화 알고리즘을 이용한 하천수위 예측모델을 구축하고 이를 실제 유역에 적용하여 모델 효율성을 평가하였다. 여기서, SVR은 하천수위를 예측하기 위한 예측모델로서 채택되었으며, 커널함수 (Kernel function)로서는 radial basis function (RBF)을 선택하였다. 최적화 알고리즘은 SVR의 최적 매개변수 (C?, cost parameter or regularization parameter; ${\gamma}$, RBF parameter; ${\epsilon}$, insensitive loss function parameter)를 탐색하기 위하여 적용되었다. 매개변수 최적화 알고리즘으로는 grid search (GS), genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC) 알고리즘을 채택하였으며, 비교분석을 통해 최적화 알고리즘의 적용성을 평가하였다. 또한 SVR과 최적화 알고리즘을 결합한 모델 (SVR-GS, SVR-GA, SVR-PSO, SVR-ABC)은 기존에 수자원 분야에서 널리 적용되어온 신경망(Artificial neural network, ANN) 및 뉴로퍼지 (Adaptive neuro-fuzzy inference system, ANFIS) 모델과 비교하였다. 그 결과, 모델 효율성 측면에서 SVR-GS, SVR-GA, SVR-PSO 및 SVR-ABC는 ANN보다 우수한 결과를 나타내었으며, ANFIS와는 비슷한 결과를 나타내었다. 또한 SVR-GA, SVR-PSO 및 SVR-ABC는 SVR-GS보다 상대적으로 우수한 결과를 나타내었으며, 모델 효율성 측면에서 SVR-PSO 및 SVR-ABC는 가장 우수한 모델 성능을 나타내었다. 따라서 본 연구에서 적용한 매개변수 최적화 알고리즘은 SVR의 매개변수를 최적화하는데 효과적임을 확인할 수 있었다. SVR과 최적화 알고리즘을 이용한 하천수위 예측모델은 기존의 ANN 및 ANFIS 모델과 더불어 하천수위 예측을 위한 효과적인 도구로 사용될 수 있을 것으로 판단된다.

• Journal of Environmental Impact Assessment
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• v.18 no.5
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• pp.271-280
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• 2009

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.235-242
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• 1998

The objective of this study is to develope a predictive model for flood forecasting in the tidal reaches of the Nakdong river and to analyze the tidal effects of major flood forecasting station of the Nakdong river by using the hydraulic flood routing. In the calibration process the optimum roughness coefficients as functions of channel reach and discharge were determined and the calibration results suggest that the unsteady hydraulic flood routing model simulated with the optimum roughness coefficients showed close agreement between the calculated and observed stage.

• Water for future
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• v.25 no.3
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• pp.115-124
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• 1992

The peak flood discharge at a downstream station and the flood travel time between a pair of dams due to a specific flood release from the upper reservoir are computed using a hydraulic river channel routing method. The study covered the whole reservoir system in the Han River. The computed peak flood discharges and the travel times between dams were correlated with the duration and the magnitude of flood release rate at the upstream reservoir, and hence a multiple regression model is proposed for each river reach between a pair of dams. The peak flood discharge at a downstream location can be converted to the peak flood stage by rating curve. Hence, the proposed regression model could be used to forecast the peak flood stage at a downstream location and the flood travel time between dams using the information on the flood release rate and duration from the upper dam.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.347-359
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• 2010

A simple web-based flood forecasting system using data from stage and rainfall monitoring stations was developed to solve the difficulty that real-time forecasting model could not get the reliabilities because of assumption of future rainfall duration and intensity. The regression model in this research could forecast future water level of maximum 2 hours after using data from stage and rainfall monitoring stations in Daejeon area. Real time stage and rainfall data were transformed from web-sites of Geum River Flood Control Office & Han River Flood Control Office based MS-Excel 2007. It showed stable forecasts by its maximum standard deviation of 5 cm, means of 1~4 cm and most of improved coefficient of determinations were over 0.95. It showed also more researches about the stationarity of watershed and time-series approach are necessary.