• Water Engineering Research
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• 제2권2호
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• pp.113-121
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• 2001

The objective of this study is to develop a prediction mode for a flood forecasting system in the downstream of the Nakdong river basin. Ranging from the gauging station at Jindong to the Nakdong estuary barrage, the hydraulic flood routing model(DWOPER) based on the Saint Venant equation was calibrated by comparing the calculated river stage with the observed river stages using four different flood events recorded. The upstream boundary condition was specified by the measured river stage data at Jindong station and the downstream boundary condition was given according to the tide level data observed at he Nakdong estuary barrage. The lateral inflow from tributaries were estimated by the rainfall-runoff model. In the calibration process, the optimum roughness coefficients for proper functions of channel reach and discharge were determined by minimizing the sum of the differences between the observed and the computed stage. In addition, the forecasting lead time on the basis of each gauging station was determined by a numerical simulation technique. Also, we suggested a model structure for a real-time flood forecasting system and tested it on the basis of past flood events. The testing results of the developed system showed close agreement between the forecasted and observed stages. Therefore, it is expected that the flood forecasting system we developed can improve the accuracy of flood forecasting on the Nakdong river.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.405-408
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• 2008

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (u-City) and/or other cities which have suffered from flood damage for a long time.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2007년도 학술대회
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• pp.181-184
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• 2007

Up to now, a lot of houses, roads and other urban facilities have been damaged by natural disasters such as flash floods and landslides. It is reported that the size and frequency of disasters are growing greatly due to global warming. In order to mitigate such disaster, flood forecasting and alerting systems have been developed for the Han river, Geum river, Nak-dong river and Young-san river. These systems, however, do not help small municipal departments cope with the threat of flood. In this study, a real-time urban flood forecasting service (U-FFS) is developed for ubiquitous computing city which includes small river basins. A test bed is deployed at Tan-cheon in Gyeonggido to verify U-FFS. Wireless sensors such as rainfall gauge and water lever gauge are installed to develop hydrologic forecasting model and CCTV camera systems are also incorporated to capture high definition images of river basins. U-FFS is based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) that is data-driven model and is characterized by its accuracy and adaptability. It is found that U-FFS can forecast the water level of outlet of river basin and provide real-time data through internet during heavy rain. It is revealed that U-FFS can predict the water level of 30 minutes and 1 hour later very accurately. Unlike other hydrologic forecasting model, this newly developed U-FFS has advantages such as its applicability and feasibility. Furthermore, it is expected that U-FFS presented in this study can be applied to ubiquitous computing city (U-City) and/or other cities which have suffered from flood damage for a long time.

• 산업경영시스템학회지
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• 제38권1호
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• pp.118-123
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• 2015

There have been various studies on measurements of flood risk and forecasting models. For river and dam region, PDF and FVI has been proposed for measurement of flood risk and regression models have been applied for forecasting model. For Bo region unlikely river or dam region, flood risk would unexpectedly increase due to outgoing water to keep water amount under the designated risk level even the drain system could hardly manage the water amount. GFI and general linear model was proposed for flood risk measurement and forecasting model. In this paper, FVI with the consideration of duration on GFI was proposed for flood risk measurement at Bo region. General linear model was applied to the empirical data from Bo region of Nadong river to derive the forecasting model of FVI at three different values of Base High Level, 2m, 2.5m and 3m. The significant predictor variables on the target variable, FVI were as follows: ground water level based on sea level with negative effect, difference between ground altitude of ground water and river level with negative effect, and difference between ground water level and river level after Bo water being filled with positive sign for quantitative variables. And for qualitative variable, effective soil depth and ground soil type were significant for FVI.

• 한국수자원학회논문집
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• 제37권2호
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• pp.145-154
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• 2004

본 연구는 비선형성이 강한 강우-유출의 특성을 고려하여 홍수시 하도의 유출을 예측하고 하천유역의 홍수예경보에 이용하기 위하여 신경망 시스템의 모형화 가능성을 검증하였다. 신경망을 이용한 실시간 하도홍수 예측모형(Neural River Discharge-Stage Forecasting Mudel; NRDFM)은 낙동강 유역의 왜관 및 진동 지점의 홍수량 예측에 적용하였다. NRDFM에 의한 하도홍수량의 왜관 및 진동 지점 예측결과를 실측치와 비교$\cdot$검토한 결과 제시한 세 가지 모형 중 NRDFM-II의 예측성능이 가장 우수하였으며, NRDFM-I 및 NRDFM-II도 충분한 예측가능성을 보여주었다. 따라서, 본 연구에서 제시한 모형은 실시간 홍수예경보로의 적용이 가능하며, 이를 통하여 효율적으로 홍수를 통제 및 관리할 수 있을 것이다.

• 물과 미래
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• 제26권4호
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• pp.117-125
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• 1993

A flood -flow management system model of river basin has been developed in this study. The system model consists of the observation and telemetering system, the rainfall forecasting and data-bank system, the flood runoff simulation system, the dam operation simulation system, the flood forecasting simulation system and the flood warning system. The Multivariate model(MV) and Meterological-factor regression model(FR) for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood-flow management. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, streamflow and other hydrological data during the past flood periods. The flood-flow management system model with SSARR model(FFMM-SR,FFMM-SR(FR) and FFMM-SR(MV)), in which the integrated operation of dams and rainfall forecasting in the basin are considered, is then suggested and applied for flood-flow management and forecasting. The results of the simulations done at the base stations are analysed and were found to be more accurate and effective in the FFMM-SR and FFMM0-SR(MV).

• 농업과학연구
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• 제46권4호
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2007년도 학술발표회 논문집
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• pp.1592-1596
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• 2007

Accuracy of flood forecasting is an important non-structural measure on the flood control and mitigation. Hence, combination of horologic model with real time error correction became an important issue. It is one of the efficient ways to improve the forecasting precision. In this work, an approach based on Kalman Filter (KF) is proposed to continuously revise state estimates to promote the accuracy of flood forecasting results. The case study refers to the Wi River in Korea, with the flood forecasting results of Xinanjiang model. Compared to the results, the corrected results based on the Kalman filter are more accurate. It proved that this method can take good effect on hydrologic forecasting of Wi River, Korea, although there are also flood peak discharge and flood reach time biases. The average determined coefficient and the peak discharge are quite improved, with the determined coefficient exceeding 0.95 for every year.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.62-71
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• 2004

There are 109 A class rivers in Japan. One purpose of river management is to reduce the flooding. For this purpose, government provides the information to public, as flood forecasting, rainfall forecasting and estimate the runoff magnitude to avoid the flood and inundation. In this paper, we introduce current situation of flood forecasting and rainfall forecasting in Japan, and we describe how to use the information of flood forecasting and rainfall forecasting in conjunction with current strategy for river management.

• 한국지능시스템학회논문지
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• 제15권4호
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• pp.437-442
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• 2005

하천에서의 홍수유출 예측은 하천의 치수적인 측면에서도 중요하다. 본 논문에서는 신경회로망 모형을 이용해서 개발된 홍수유출 예측 시스템의 적용성을 검토하였다. 입력층에는 강우자료와 홍수량 자료를 출력층에는 홍수유출량이 예측되도록 구성하였다. 홍수유출 예측 시스템 구성시 예측모형 선정을 위해 신경회로망 모형과 상태공간 모형을 이용하여 홍수시 실시간 하천유출량 예측을 수행하였다. 두 모형의 예측결과 비교시 신경회로망 모형이 실시간 홍수량 예측에 적합한 모형으로 선정되었다. 신경회로망 모형은 Web 상에서 사용이 가능하게 변환하여 홍수유출 예측시스템의 기본모형으로 개발하였다.