• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.969-980
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• 2023

Since the 2000s, ensemble streamflow prediction (ESP) has been actively utilized in South Korea, primarily for hydrological forecasting purposes. Despite its notable success in hydrological forecasting, the original objective of enhancing water resources system management has been relatively overlooked. Consequently, this study aims to demonstrate the utility of ESP in water resources management by creating a simple hypothetical exercise for dam operators and applying it to actual multi-purpose dams in South Korea. The hypothetical exercise showed that even when the means of ESP are identical, different costs can result from varying standard deviations. Subsequently, using sampling stochastic dynamic programming (SSDP) and considering the capacity-inflow ratio (CIR), optimal release patterns were derived for Soyang Dam (CIR = 1.345) and Chungju Dam (CIR = 0.563) based on types W and P. For this analysis, Type W was defined with standard deviation equal to the mean inflow, and Type P with standard deviation ten times of the mean inflow. Simulated operations were conducted from 2020 to 2022 using the derived optimal releases. The results indicate that in the case of Dam Chungju, more aggressive optimal release patterns were derived under types with smaller standard deviations, and the simulated operations demonstrated satisfactory outcomes. Similarly, Soyang Dam exhibited similar results in terms of optimal release, but there was no significant difference in the simulation between types W and P due to its large CIR. Ultimately, this study highlights that even with the same mean values, the standard deviation of ESP impacts optimal release patterns and outcomes in simulation. Additionally, it underscores that systems with smaller CIRs are more sensitive to such uncertainties. Based on these findings, there is potential for improvements in South Korea's current operational practices, which rely solely on single representative values for water resources management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.264-272
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• 2011

The objective of this study was to develop a model for predicting long-term runoff in a basin using the ensemble streamflow prediction (ESP) technique and review its reliability. To achieve the objective, this study improved not only the ESP technique based on the ensemble scenario analysis of historical rainfall data but also conventional ESP techniques used in conjunction with qualitative climate forecasting information, and analyzed and assessed their improvement effects. The model was applied to the Geum River basin. To undertake runoff forecasting, this study tried three cases (case 1: Climate Outlook + ESP, case 2: ESP probability through monthly measured discharge, case 3: Season ESP probability of case 2) according to techniques used to calculate ESP probabilities. As a result, the mean absolute error of runoff forecasts for case 1 proposed by this study was calculated as 295.8 MCM. This suggests that case 1 showed higher reliability in runoff forecasting than case 2 (324 MCM) and case 3 (473.1 MCM). In a discrepancy-ratio accuracy analysis, the Climate Outlook + ESP technique displayed 50.0%. This suggests that runoff forecasting using the Climate Outlook +ESP technique with the lowest absolute error was more reliable than other two cases.

• Water for future
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• v.27 no.1
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• pp.141-150
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• 1994

The purpose of this study is develope the algorithm of channel routing model which can be used for flood forecasting. In routing model, the hydrulic technique of the implicit scheme in the dynamic equation is chosen to route the unsteady varied flow. The channel routing model is connected with conceptual watershed model which is able to compute the flood hydrograph from each subbasin. The comparative study shows that the conceptual model can simulate the watershed runoff accurately. As a result of investigating the channel routing model, the optimal weighting factor $\theta$ which fixes two points between time line is selected. And also, the optimal error tolerance which satisfies computing time and converge of solution is chosen.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1455-1459
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• 2006

우리나라 수자원 관리에서 여름 유량은 이수 및 치수 측면에서 매우 중요한 역할을 한다. 이러한 점에서 여름유량의 예측 가능성을 검토하는 것은 수자원 관리에 유연성을 주는 동시에 상대적으로 위험도를 저감시킬 수 있는 역할을 할 수 있다. 따라서 본 연구의 목적은 여름 계절 유량을 대상으로 기상인자와의 상관성 분석을 통해 유량 예측을 위한 수문기상정보(hydroclimatics)를 전 지구적으로 검토하고 최종적으로 불확실성을 고려할 수 있는 Ensemble예측을 실시하고자 한다. Ensemble예측은 설정 가능한 입력 자료를 통하여 다수의 출력자료를 얻는 방법론으로서 불확실성이 큰 기상 및 수문기상자료 분석에 주로 이용되고 있다. 본 연구에서는 해수면온도(sea surface temperature), 해수면기압(sea level pressure)과 방출장파복사에너지(outgoing longwave radiation)를 주요 기상인자로 고려하였으며 예측모형으로서는 Cross Ensemble(out of bagging)방법에 근거한 Support Vector Machine 모형을 이용하였다. 분석결과 주요 기상인자와 50%이상의 상관관계를 보이고 있으며 다소 합리적인 예측 결과를 제시하여 주고 있어 수자원관리를 위한 보조수단으로 이용이 가능할 것으로 사료된다.

• The Journal of the Korea Contents Association
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• v.7 no.3
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• pp.140-151
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• 2007

In this study, we attempted to develop a watershed runoff index subject to main control points by dividing the Geum River basin into 14 sub-basins. The Yongdam multipurpose dam Daecheong multipurpose dam and Gongju gage station were selected to serve as the main control points of the Geum River basin, and the observed flow of each control point was calculated by the discharge rating curve, whereas the simulated flow was estimated using the Rainfall Runoff Forecasting System (RRFS), user-interfaced software developed by the Korea Water Corporation, based on the Streamflow Synthesis and Reservoir Regulation (SSARR) model developed by the US Army Corps of Engineers. This study consisted of the daily unit observed flow and the simulated flow of the accumulated moving average flow by daily, 5-days, 10-days, monthly, quarterly and annually, and normal monthly/annually flow. We also performed flow duration analysis for each of the accumulated moving average and the normal monthly/annually flows by unit period, and abundant flow, ordinary flow, low flow and drought flow estimated by each flow duration analysis were utilized as watershed runoff index by main control points. Further, as we determined the current flow by unit period and the normal monthly/annually flow through the drought and flood flow analysis subject to each flow we were able to develop the watershed runoff index in a system that can be used to determine the abundance and scarcity of the flow at the corresponding point.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.571-583
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• 2024

Forecasting dam inflows in the medium to long term is crucial for effective dam operation and the prevention of water-related disasters such as floods and droughts. However, the increasing frequency of extreme weather events due to climate change has made hydrological forecasting more challenging. Since 2000, seasonal weather forecasts, which provide predictions for weather variables up to about seven months ahead, and their hydrological interpretation, known as Seasonal Flow Forecasts (SFFs) have gained significant global interest. This study utilises seasonal weather forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), converting them into inflow forecasts using a hydrological model for 12 multipurpose dams in South Korea from 2011 to 2020. We then compare the performance of these SFFs with the Ensemble Streamflow Prediction (ESP). Our results indicate that while SFFs are more effective for short-term predictions of 1-2 months, ESP outperforms SFFs for long-term predictions. Seasonally, the performance of SFFs is higher in October-November but lower from December to February. Moreover, our findings demonstrate that SFFs are highly effective in quantitatively predicting dry conditions, although they tend to underestimate inflows under wet conditions.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1061-1072
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• 2005

A hydrological grey model is developed to forecast short-term river runoff from the Naju watershed located at upstream of the Youngsan estuary dam in Korea. The runoff of the Naju watershed is measured in real time at the Naju streamflow gauge station, which is a key station for forecasting the upstream inflow and operating the gates of the estuary dam in flood period. The model's governing equation is formulated on the basis of the grey system theory. The model parameters are reparameterized in combination with the grey system parameters and estimated with the annealing-simplex method In conjunction with an objective function, HMLE. To forecast accurately runoff, the fifth order differential equation was adopted as the governing equation of the model in consideration of the statistic values between the observed and forecast runoff. In calibration, RMSE values between the observed and simulated runoff of two and six Hours ahead using the model range from 3.1 to 290.5 $m^{3}/s,\;R^2$ values range from 0.909 to 0.999. In verification, RMSE values range from 26.4 to 147.4 $m^{3}/s,\;R^2$ values range from 0.940 to 0.998, compared to the observed data. In forecasting runoff in real time, the relative error values with lead-time and river stage range from -23.4 to $14.3\%$ and increase as the lead time increases. The results in this study demonstrate that the proposed model can reasonably and efficiently forecast runoff for one to six Hours ahead.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.408-412
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• 2006

Growing needs for efficient management of water resources urge integrated management of whole basin. As one of the tools for supporting above tasks, this study aims to indicate a hydrologic model that can simulate the streamflow discharges at some control points located both upper and down stream of dams. For the development and utilization of non 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 basis of continuous daily runoff model for forecasting short and long-term national river flows in this paper.