• 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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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Water for future
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• v.28 no.6
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• pp.193-202
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• 1995

The subjective research attempts to apply a rainfall-runoff model capable of considering time-variation of soil water contents which are highly correlated to the river flows on the qpqyungchang river basin and to evaluate its performance for flow forecasting. The model used in this study is a physically-based conceptual time-continuous model, which is composed of the Sacramento soil moisture accounting model and the nonlinear multiple conceptual reservoirs model. The daily precipitation and evaporation data for 7 years and for 3 years were used for the parameter estimation and the model verification, respectively. As a result, the flows including a significant flood event were well simulated, and the cross-correlation coefficient between observed flows and computed flows for the verification periods was 0.87, but in general computed flows were underestimated for the low-flow periods. Also, the effects of precipitation and soil water content to the river flows were analysed for the flood and the drought.

• Water for future
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• v.24 no.4
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• pp.49-58
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• 1991

Flood routing in the Youngsan River was performed for the flood event of July, 1989 by two finite difference methods. The Saint Venant eq., a kind of hyperbolic partial differential equation is employed as governing equation and the explicit scheme (Leap Frog) and implicit scheme (Preissmann) are used to discretize the GE. As for the external boundary conditions, discharge and tidal elevation are upstream and downstream BC, respectively and estuary dam is included in internal BC. Lateral inflows and upstream discharges are the hourly results from storage function method, At Naju station, a Relatively upstream points in this river, the outputs are interpreted as good ones by comparing two numerical results of FDMs with the observed data and the calibrated results by storage function method. and two computational results are compared at the other sites, from middle stream and downstream points, and thus are considered reliable. Therefore, we can conclude from this research that these numerical models are adaptable in simulating and forecasting the flood in natural channels in Korea as well as existing hydrologic models. And the study about optimal gate control at the flood time is expected as further study using these models.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.485-500
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• 2002

Hydraulic model was developed to analyze the complex flow due to channel structures, tide, and tributaries in the lower Han river and Imjin river. DWOPER-2K model which can automatically process the data transformation in the model was developed as the 1-D hydraulic routing model. Observed data in tidal zone and the recent channel geometry data were collected for hydraulic model. And the flow over the Jamsil and Singok submerged weir was analyzed properly and roughness coefficient was optimized to each regions and each discharges. By the results of verification of the model, the model developed in this study may contribute to improvement of the accuracy of flood forecasting and channel management because this model can efficiently and properly analyze the various kind of flow occurred in the region of the lower Han river and Imjin river.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.501-509
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• 2006

The safety of levee that depends on the river flood elevation has been regarded as very important keys to build up various flood prevention systems. However, deterministic methods for computation of water surface profile cannot reflect the effect of possible inaccuracies in the input parameters. The purpose of this study is to develop a methodology of uncertainty computation of design flood level based on steady flow analysis and Monte Carlo simulation. This study addresses the uncertainty of water surface elevation by Manning's coefficients, design discharges, river cross sections and boundary condition. Monte Carlo simulation with the variations of these parameters is performed to quantify the variations of water surface elevations in a river. The proposed model has been applied to the Kumho-river. The reliability analysis was performed within 38.5 km (95 sections) reach considered the variations of the above-mentioned parameters. Overtopping risks were evaluated by comparing the elevations of the flood condition with the those of the levees. The results show that there is a necessity which will raise the levee elevation between 1 cm and 56 cm at 7 sections. The model can be used for preparing flood risk maps, flood forecasting systems and establishing flood disaster mitigation plans as well as complement of conventional levee design.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.398-402
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• 2012

최근 집중호우 및 홍수범람으로 인한 연평균 피해는 침수면적 26,757.17 ha, 수리시설 파괴 1,122개소, 그로 인한 피해액 약 580억원 등으로 집계되었으며 이상기후로 인한 집중호우 빈도 증가에 따른 잦은 홍수범람으로 그 피해액도 늘어나고 있는 것으로 조사되었다(국가재난정보센터). 이와 같은 피해를 최소화하기 위해서 홍수를 미리 예보하고 경보하는 시스템이 필요하며 시스템의 정확도 역시 중요하다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.3-3
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• 2016

기후변화로 태풍, 집중호우 등의 발생빈도가 높아져 도시지역에서 홍수범람으로 인한 인명피해와 재산상의 손실이 반복되고 있으며, 이로 인한 사회기반시설에 대한 피해가 급증하고 있다. 따라서 이를 효과적으로 방어할 수 있는 구조적, 비구조적 내배수 홍수방어 대책의 수립이 시급한 실정이다. 도시유역에서의 침수피해는 내부배제 불량에 따른 내수침수가 대부분을 차지하고 있으며 효과적인 내수침수예측 및 홍수피해 저감을 위한 방재시스템 구축을 위해서는 내외수를 연계한 침수 예측 선진화 기술개발이 필요하다. 따라서 본 섹션에서는 레이더 및 위성 정보를 활용한 초단기 또는 단기 강우예측 기술개발, 하수관망 단순화 자동화 기술 개발, 베이지안 모형과 연계한 SWMM모형의 매개변수최적화, 내수침수 대응을 위한 동적의사결정 모형 등의 핵심기술을 통합한 내외수를 연계한 도시홍수예보시스템 개발내용과 실강우사상에 대한 적용성 검토 결과를 소개하고자 한다. 본 연구의 성과는 실시간 또는 예측강우분석을 통하여 돌발홍수로 인한 내수침수피해에 대한 골들타임을 확보하고 대피지구 선정 및 주민의 대피이동 정보제공 등 도시홍수로부터 발생하는 피해에 대한 최적화된 선제적 대응체계 구축에 활용도가 높을 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.320-320
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• 2021

홍수피해가 빈발하는 도시 및 소규모 산지 유역에서와 같이 지체시간이 짧은 유역에서 국지적으로 발생하는 돌발홍수는 우량계와 기존 하천유역 예보시스템만으론 예보가 불가능하다. 동일한 강우에서도 지역에 따라 침수시간이나 침수심이 달라지기 때문에 정확한 돌발홍수예보를 위해서는 지역에 따른 침수특성과 유속특성을 달리 고려해야 한다. '골든타임 확보를 위한 유역 시공간 상세 홍수예보기술 개발(환경부)'에서 개발한 '국지 돌발홍수예측 시스템'은 지역별 검증된 침수특성과 유속특성의 관계식을 산정하여 돌발홍수예보 기준을 설정하였다. 그리고 도달시간이 짧은 도시 및 산지에서 홍수예보 선행시간을 확보하기 위해 강우레이더 기반 돌발홍수 예측 시스템을 구축하여 시범 운영 중이다. 그러나 도시·산지 중소하천유역 등 홍수예보 취약지역에 대한 돌발홍수예보 정확도를 제고하기 위해서는 기 설정된 돌발홍수위험 예보 기준을 정밀하게 평가·검증·개선 할 수 있는 실증 체계가 반드시 필요하다. 이러한 배경에서 본 연구에서는 2021년부터 3개년 동안 홍수예보 취약지역에 강우레이더와 경제적 IoT 관측센서 정보를 기반으로 돌발홍수예보 실증기술을 개발하여 전국 돌발홍수예보 실용화 기반 구축하고자 한다. 홍수피해 취약지역인 도심지, 산지·계곡, 해안지역에 실증 테스트베드를 선정하고 강우레이더-IoT 실증 관측망을 구축하여 돌발홍수예보 기술 실증과 돌발홍수 위험기준 설정 가이드라인을 마련하고자 한다. 더불어 도시 중소하천유역 홍수예보 활용을 위한 소형강우레이더 강우량 정확도 개선 기술 개발과 홍수기 강우레이더 기반 홍수예보 관-연 협업 시범 운영을 추진할 계획이며, 최종적으로는 강우레이더와 IoT 정보 기반 돌발홍수 실증 시스템을 구축 운영하고자 한다.