• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.429-439
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• 2009

When Catastrophic extreme flood occurs due to dam break, the response time for flood warning is much shorter than for natural floods. Numerical models can be powerful tools to predict behaviors in flood wave propagation and to provide the information about the flooded area, wave front arrival time and water depth and so on. But flood wave propagation due to dam break can be a process of difficult mathematical characterization since the flood wave includes discontinuous flow and dry bed propagation. Nevertheless, a lot of numerical models using finite volume method have been recently developed to simulate flood inundation due to dam break. As Finite volume methods are based on the integral form of the conservation equations, finite volume model can easily capture discontinuous flows and shock wave. In this study the numerical model using Riemann approximate solvers and finite volume method applied to the conservative form for two-dimensional shallow water equation was developed. The MUSCL scheme with surface gradient method for reconstruction of conservation variables in continuity and momentum equations is used in the predictor-corrector procedure and the scheme is second order accurate both in space and time. The developed finite volume model is applied to 2D partial dam break flows and dam break flows with triangular bump and validated by comparing numerical solution with laboratory measurements data and other researcher's data.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.78-88
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• 2006

This paper is to apply Geographical Information System (GIS) supported Geomorphoclimatic Instantaneous Unit Hydrograph (GCIUH) approach for the calculated flash flood trigger rainfall of the mountainous area. GIS techniques was applied in geography data construction such as average slope, drainage area, channel characteristics. Especially, decided stream order using GIS at stream order decision that is important for input variable of GCIUH. We compared the GCIUH peak discharge with the existing report using the design storm at Chundong basin($14.58km^2$). The results showed that derived the GCIUH was a very proper method in the calculation of mountaunous discharge. At the Chundong basin, flash flood trigger rainfall was 12.57mm in the first 20 minutes when the threshold discharge was $11.42m^3/sec$.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1494-1498
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• 2009

세계기상기구(WMO)에 의하여 홍수예경보시스템의 평가를 위하여 개발된 MOFFS Ver.3의 개선을 위하여 특정유역의 홍수방어체계의 취약성을 평가하고 홍수방어시스템의 완성도를 높이기위한 DSS-FOP를 개발하였다. DSS-FOP는 지점별, 홍수사상별로 예보시스템 운영결과를 간단한 평가양식에 나타내는 시스템이며, 홍수방어구조물(Flood control infra)과 홍수조절운영(Flood control operation)으로 분류한다. 홍수방어구조물은 수문관측, 홍수방어구조물, 홍수조절지휘소로, 홍수조절 운영은 자료처리 및 전송, 홍수예측모형, 예경보발령의 총 6가지 주요평가항목과 하부의 23가지 세부평가항목으로 구성하였다. 점수부여체계는 최대점수, 목표점수, 성과점수, 부족점수, 취약점수의 산정을 통하여 구조물인프라와 운영측면에서의 시스템취약부분을 평가 진단할 수 있도록 하였다. 개발된 DSS-FOP를 이용하여 국내의 한강유역과 UN/ESCAP 태풍위원회의 회원국인 태국의 Khlong U-Taphao 유역을 대상으로 적용하고 그 결과를 비교 평가하였다. 한강유역의 경우 하천정보센터 신설 및 조직강화로 인적자원측면에서 높은 성과점수를 보였으며, 향후 수문레이더 설치 등으로 관측분야에서의 개선이 기대된다. 태국의 Khlong U-Taphao 유역의 경우 목표수준을 다소 조정할 필요가 있으며, 비상행동계획의 마련이 시급하다. 더불어 홍수방어구조물에 대한 지속적인 투자가 필요한 상황이다. 이러한 DSS-FOP의 평가결과는 국가별, 유역별, 호우사상별로 관리되며, 태풍위원회 회원국의 적용 및 기술지침의 작성을 위해 많은 평가 및 조사가 축적되어야 한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.36-52
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• 2020

This study performed 2-D(two-dimensional) hydrodynamic analysis using EFDC in the Nakdong River. For the simulation of the flood season and non-flood season, the measured data including water level, weir outflow and tributary inflow were used, and the accuracy and applicability of the model were verified by comparing the measured water level and computed one. In addition, statistical quantitative assessment of the model performance was performed by estimating PBIAS, RSR, and RMSE for the computed water level. Then, the average velocity for each section between weirs was calculated by applying constant discharge conditions, and it was compared and verified with the measured velocity by Hydrological Survey Center. In this study, a simple method for estimating the arrival time was proposed, and it is expected that it will be practically applicable in field practices such as flood forecasting and warning.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.449-453
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• 2007

Has been being operated since 1974, recently, the flood forecasting and warning system is applied in almost all the rivers in Korea, and the Storage Function Method(SFM) is used for flood routing. The SFM which was presented by Toshimitsu Kimura(1961) routes floods in channels and basins with the storage function as the basic equation. A watershed is devided into two zone, runoff and percolation area and Runoff is occured when cumulated rainfall is not exceed saturation rainfall, but exceed, runoff is occured from percolation area, too. Runoff area is given and not changed, runoff ratio is constant. In routing process, runoff from runoff and percolation area is routed seperately with nonlinear cenceptual reservior having same characteristics and it is unreasonable assumption. Modified SFM is proposed with storage function and continuity Equation which has no assumption for routing process and effective rainfall is calculated by SCS Method. For Wi Stream, comparision of Kimura and Modified SFM is conducted and It could be seen that Modified SFM is more improvemental and easily applicable method.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.

• Journal of Korea Water Resources Association
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• v.44 no.11
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• pp.875-887
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• 2011

The objective of this study is to develop and evaluate a Korean threshold runoff computation method. The selected study area is the Han-River basin and the stream channels in the study area are divided into 3 parts; natural channel and artificial manmade channel for small mountainous catchments, and main channel for master stream. The threshold runoff criteria for small streams is decided to 0.5 m water level increase from the channel bottom, which is the level that mountain climbers and campers successfully escape from natural flood damage. Threshold runoff values in natural channel of small mountainous area are computed by the results from the regional regression analysis between parameters of basin and stream channel, while those in artificial channel of small mountainous area are obtained from the data of basin and channel characteristics parameter. On the other hand, the threshold runoff values for master channel are used the warning flood level that is useful information for escaping guideline for riverside users. For verification of the threshold runoff computation method proposed in this study, three flash flood cases are selected and compared with observed values, which is obtained from SCS effective rainfall computation. The 1, 3, 6-hour effective rainfall values are greater than the corresponding threshold runoff values represents that the proposed computation results are reasonable.

• 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.

• Journal of Korea Water Resources Association
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• v.49 no.12
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• pp.981-993
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• 2016

This study explains the GIS database of flood inundation area developed for Samsung-1 Drainage Sector, Seoul, Korea. The XP-SWMM dual drainage model was developed for the study area, and the time series observed at the watershed outlet was used to obtain the watershed time of concentration and to calibrate the XP-SWMM model. The rainfall scenario was developed by dividing the 40 minute watershed time of concentration into two 20-minute time steps and then applying the gradually increasing 5 mm/hr interval rainfall intensity to each of the time step up to 200 mm/hr, which is the probable maximum precipitation of the study area. The developed rainfall scenarios was used as the input of the XP-SWMM model to obtain the database of the flood inundation area. The analysis on the developed GIS database revealed that: (1) For the same increment of the rainfall, the increase of the flooded area can be different, and this was caused by topographic characteristics and spatial formation of pipe network of the study area; (2) For the same flooded area, the spatial extent can be significantly different depending on the temporal distribution of rainfall; and (3) For the same amount of the design rainfall, the flood inundation area and the extent can be significantly different depending on the temporal distribution of rainfall.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.