• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.164-164
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• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.449-457
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• 1997

This study attempts to develop a stochastic-dynamic real-time flow forecasting model for an event-orient watershed storage function model (SFM), which has been used as an official flood computation model in Korea, and to evaluate its performance for real-time flow forecast. The study area is the 747.5$\textrm{km}^2$ Hwecheon basin with outlet at Gaejin and the 8 single flow events during 1983-1986 are selected for comparison and verification of model parameter and model performance. The used model parameters in this study are the same values on field work. It is shown that results from the existing model highly depend on the events, but those from the developed model are stable and well predict the flows for the selected flood events. The coefficient of model efficiency between observed and predicted flows for the events was above 0.90. It is concluded that the developed model that can consider model and observation uncertainties during a flood event is feasible and produces reliable real-time flow forecasts on the area.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.159-162
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• 2005

Urban flooding is usually caused by the surcharge of storm sewers. For that reason, domestic studies about urban flooding are concentrated on the simulation of urban drainage system. However these approaches that find the pipes which have insufficient drainage capacity are very approximate and unreasonable ways. In this study, an accurate mathematical modeling is developed to analyze the impacts of an urban inundation for both flood prevention and flood-loss reduction planning and it is verified by using the simulation of July 2001 flooding in Seoul. The result of this study can be used to construct fundamental data for a flood control plan and establish a urban flood forecasting/warning system.

• Water for future
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• v.27 no.3
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• pp.145-158
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• 1994

The linear reservoir rainfall-runoff system was developed as a rainfall-runoff event simulation model. It was achieved from large modification of runoff function method. There are six parameters in the model. Hydrologic losses consist of some quantity of initial loss and some ratio of rainfall intensity followed by initial loss. The model has analytical routing equations. Hooke and Jeeves algorithm was used to model calibration. Parameters were estimated for flood events from '84 to '89 at Seomyeon and Munmak stream gauges, and the trends of major parameters were analyzed. Using the trends, verifications were performed for '90 flood event. Because antecedent fainfalls affect initial loss, future researches are required on such effects. The estimation method of major parameters should also be studied for real-time forecasting.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.145-152
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• 2006

In the present study, a nonlinear model of rainfall-runoff process using Artficial Neural networks(ANNs) which have no consideration on the physical parameter for the basin was developed at Naju station which is the main stream of Yeongsan-river, and Sunam station which is the main stream of Hwangryong-river. The result from the model of ANN_NJ_9 at the Naju station revealed the best result of the rainfall-runoff process, while the model of ANN_SA_9 for the Sunam station. Also, GUI_FFS developed in the research showed the $R^2$ of more than 0.98 between the observed and predicted values using the rainfall and runoff in the respective stations. Therefore, the GUI_FFS might be expected that it can play a role for the high reliability to operate and manage the water resources and the design of river plan more efficiently in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.140-140
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• 2021

Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.

• Journal of Korean Society on Water Environment
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• v.25 no.4
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• pp.573-579
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• 2009

The performance of Rainfall-Runoff Forecasting System (RRFS), the watershed management model for the Geum river basin, is evaluated based on the agreement between the simulated and observed hydrographs and the behavioral characteristics of the flow-duration curves. As a result, the simulated hydrographs are well agreed with the observed ones except high flow discharges. It is inferred that most of the errors in the simulated hydrographs are due to the misestimation of agricultural water use in $2^{nd}$ quarter and the discrepancy of the peak discharges in $3^{rd}$ quarter. It is however judged that RRFS would give the reliable runoff hydrographs from the point of view of continuous model application. And simulated flow-duration curves and flow-duration coefficients are also similar to the observed ones except flood flow region. From the above result it is confirmed that the construction of Yongdam dam improves the state of flow-duration curve at the Gongjoo station.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.583-586
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• 2007

본 논문에서는 삼수원신안강모형(三水源新安江模型)으로 한국 위천유역에 대하여 홍수예보를 모의하였다. 결과 신안강모형(新安江模型)은 위천유역의 홍수를 비교적 정확하게 모의하였고 평균 모형 효율성 계수는 0.93이나 되었으므로 홍수예보를 적용하는데 적합하다. 신안강모형(新安江模型)은 습윤 반습윤지구의 축만유출(蓄滿流出)의 모형으로서 초기 토양함수량이 풍부한 흥수에 대해서는 정확도가 매우 높다. 그러므로 홍수예보를 하는데 있어서 신안강모형(新安江模型)이 광범한 실용가치가 있다고 예상된다.