• Journal of Korea Water Resources Association
• /
• v.37 no.1
• /
• pp.67-75
• /
• 2004

In this study, real-time forecasting model(Neural Dam Inflow Forecasting Model; NDIFM) based on neural network to predict the dam inflow which is occurred by flood runoff is developed and applied to check its availability for the operation of multi-purpose reservoir Developed model Is applied to predict the flood Inflow on dam Nam-Gang in Nak-dong river basin where the rate of flood control dependent on reservoir operation is high. The input data for this model are average rainfall data composed of mean areal rainfall of upstream basin from dam location, observed inflow data, and predicted inflow data. As a result of the simulation for flood inflow forecasting, it is found that NDIFM-I is the best predictive model for real-time operation. In addition, the results of forecasting used on NDIFM-II and NDIFM-III are not bad and these models showed wide range of applicability for real-time forecasting. Consequently, if the quality of observed hydrological data is improved, it is expected that the neural network model which is black-box model can be utilized for real-time flood forecasting rather than conceptual models of which physical parameter is complex.

• Journal of Korean Society on Water Environment
• /
• v.39 no.1
• /
• pp.38-45
• /
• 2023

In this study, the removal efficiency of road sweeping and sand filter facility for removing total suspended solid (TSS) as nonpoint source pollution from expressway was evaluated for the last 10 years (2012~2021) using ROADMOD. ROADMOD is a screening level model and was calibrated for runoff rate and TSS loading both at the inlet, which is the loading from the drainage area, and the outlet, from the sand filter facility. The drainage area is 715 m² and the dimensions of sand filter facility are 1.5 m (wide) × 3.8 m (length) × 1.5 m (depth). The monitoring period for model calibration was the rainfall event during Aug. 31~Sep. 1, 2021 and the amount of rainfall was 74.5 mm. As a result of calibration, the determination coefficients (R²) of the flow rate were 0.66 and 0.86, for the inlet and outlet, respectively, and those of TSS loading were 0.50 and 0.84, for the inlet and outlet, respectively. Considering that ROADMOD is a screening level model, the calibration results were reasonable to evaluate the best management practices (BMPs) on the expressway. Using ROADMOD simulation results for 2012~2021, the average yearly runoff rate from the expressway was 82% and removal efficiency was 9% for road sweeping, 35% for sand filter facility, and 39% for both road sweeping and sand filter facility.

• Journal of Korea Water Resources Association
• /
• v.48 no.5
• /
• pp.409-423
• /
• 2015

In this study the very short-term rainfall forecasting and storm water forecasting using the weather radar data were implemented in an urban stream basin. As forecasting time increasing, the very short-term rainfall forecasting results show that the correlation coefficient was decreased and the root mean square error was increased and then the forecasting model accuracy was decreased. However, as a result of the correlation coefficient up to 60-minute forecasting time is maintained 0.5 or higher was obtained. As a result of storm water forecasting in an urban area, the reduction in peak flow and outflow volume with increasing forecasting time occurs, the peak time was analyzed that relatively matched. In the application of storm water forecasting by radar rainfall forecast, the errors has occurred that we determined some of the external factors. In the future, we believed to be necessary to perform that the continuous algorithm improvement such as simulation of rapid generation and disappearance phenomenon by precipitation echo, the improvement of extreme rainfall forecasting in urban areas, and the rainfall-runoff model parameter optimizations. The results of this study, not only urban stream basin, but also we obtained the observed data, and expand the real-time flood alarm system over the ungaged basins. In addition, it is possible to take advantage of development of as multi-sensor based very short-term rainfall forecasting technology.

• Journal of Wetlands Research
• /
• v.16 no.1
• /
• pp.19-32
• /
• 2014

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.619-622
• /
• 2005

적용대상 유역은 낙동강수계로 하였으며 소유역 분할은 총 25개로 하였으며, 강우관측소의 선정과 Thiessen 계수의 산정은 최근에 한국수자원공사에서 새로 추가한 강우관측소를 위주로 대상 연도별로 달리하여 강우관측소를 선정하였다. 강우자료의 결측치는 RDS 방법을 사용하여 보완하였다. 대상연도별 소유역별로 일간 유역 평균 강우량을 산정하였다. 적용 모형의 선정은 한국수자원공사 실무부서에서의 적용사례가 빈번한 SSARR 모형을 최종적으로 선정하였다. SSARR 모형의 입력자료를 물리적 매개변수, 수문기상 매개변수 및 내부처리 매개변수로 구분하여 구축하였고 매개변수의 민감도분석과 함께 모형의 보정을 실시하였다. 민감도 분석 결과, 유역유출과 관련된 매개변수에서는 고수시와 저수시의 경우 지표수와 복류수의 분리하는 매개변수에서 민감도가 크게 나타났다. 저수시의 경우 지하수 중 회귀지하수가 차지하는 비율이 크게 나타났고, 지표수, 복류수, 지하수 및 회귀지하수의 저류시간에서 비교적 큰 민감도를 나타내었다. 1983년부터 2003년까지 21개년에 걸쳐 25개 소유역별로 일평균 자연유출량을 산정하여 이를 이용한 반순, 순, 월 및 연평균 자연유출량을 산정하였다.

• Environmental Engineering Research
• /
• v.13 no.3
• /
• pp.107-111
• /
• 2008

Water quality contamination issues are of critical concern to human health, whilst pesticide release generated from irrigated land should be considered for protecting natural habitats and human health. This paper suggests new method for evaluation and analysis using the GIS technique based on integrated spatial modeling framework. The pesticide use on irrigated land is a subset of the larger spectrum of industrial chemicals used in modern society. The behavior of a pesticide is affected by the natural affinity of the chemical for one of four environmental compartments; solid matter, liquid, gaseous form, and biota. However, the major movements are a physical transport over the ground surface by rainfall-runoff and irrigation-runoff. The irrigated water carries out with the transporting sediments and makes contaminated water by pesticide. This paper focuses on risk impact identification and assessment using GIS technique. Also, generated data on pesticide residues on farmland and surface water through GIS simulation will be reflected to environmental research programs. Finally, this study indicates that GIS application is a beneficial tool for spatial pesticide impact analysis as well as environmental risk assessment.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.382-382
• /
• 2023

기존의 GRM(Grid based rainfall-Runoff Model)에서는 강우-유출 사상에 대한 유출 모의를 주요 대상으로 하였다. 본 연구에서는 GRM 모형에서 연속형 모의가 가능하도록 차단, 증발산, 융설을 모의할 수 있는 모듈을 개발하였다. 차단은 LAI의 연최댓값과 해당월의 값의 비율을 이용해서 계산하며, 증발산은 Blaney-Criddle, Hamon, Hargreaves, Priestly-Taylor 방법을 적용하였다. 융설은 Anderson에 의해서 제안된 방법을 적용하였다. 연속형 모의를 위한 모델 매개변수 설정 인터페이스를 추가하였으며, 기온, 일사량, 일조시간 등의 기상자료를 입력할 수 있게 하고, 계산된 각 수문성분을 출력할 수 있도록 GRM 모형의 입력과 출력 모듈을 개선하였다. 충주댐 유역을 대상으로 개선된 모형을 적용하였다. 공간자료의 해상도는 500m × 500m로 구축하였으며, 수문학적 지형정보와 토양도, 토지피복도를 구축하였다. 기상자료를 강수량, 일최고 기온, 일최저 기온, 일조시간, 일사량을 적용하였다. 증발산은 Hargreaves 방법을 이용하여 모의하였다. 모의 기간은 2001년 ~ 2018년이며, 이 중 2004년까지의 4년은 모델 warming up 기간으로 하고, 적합도 평가는 2005년 ~ 2018년의 모의결과를 이용하였다. 충주댐 유입량 모의결과를 관측값과 비교하였을 때 Nash-Sutcliffe model efficiency coefficient(NSE) 0.84, 상관계수 0.92, 총용적 오차는 0.26%를 나타내어 관측유입량을 잘 재현하였다. 그러므로 본 연구에서 개발된 차단, 증발산, 융설 모의 기법은 적절히 구현된 것으로 판단되며, GRM을 이용한 연속형 모의가 가능한 것으로 나타났다. 향후 연구에서는 좀 더 다양한 유역에 대해 GRM을 이용한 연속형 유출모의 결과를 평가할 필요가 있다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.33 no.1
• /
• pp.157-170
• /
• 2013

In this study, flood runoff characteristics is analyzed according to subbasin divisions by physically based rainfall-runoff model and appropriate number of subbasin divisions is suggested for midsize test basins. The Clark method, a lumped model in HEC-HMS, and the ModClark method, a semi-distributed model are used to simulate rainfall-runoff processes on Andong-reservoir basin, Imha-reservoir basin, and Pyeongchang river basin. The test basins were divided into nine subdivision cases by equal-area subdivision method such as single basin, 3, 5, 6, 7, 9, 10, 12, and 15 subbasins, and compared the simulated and observed values in terms of the peak flow and the peak time. The simulation results indicated that the peak flows tended to increase and the peak time shifted earlier as the number of subdivisions increased and this tendency weakened after the certain number of subdivisions. In this research, the specific number of subdivision was defined as the minimum number of subdivision considering both peak flow and peak time. Consequently, the minimum number of subdivisions is determined as 5 for Andong and Imha reservoir basins and 7 for Pyeongchang river basin.

• Journal of Korea Water Resources Association
• /
• v.51 no.1
• /
• pp.81-88
• /
• 2018

The rainfall-runoff model made of sewer networks in the urban area is vast and complex, making it unsuitable for real-time urban flood forecasting. Therefore, the rainfall-runoff model is constructed and simplified using the sewer network of Daerim baisn. The network simplification process was composed of 5 steps based on cumulative drainage area and all parameters of SWMM were calculated using weighted area. Also, in order to estimate the optimal simplification range of the sewage network, runoff and flood analysis was carried out by 5 simplification ranges. As a result, the number of nodes, conduits and the simulation time were constantly reduced to 50~90% according to the simplification ranges. The runoff results of simplified models show the same result before the simplification. In the 2D flood analysis, as the simplification range increases by cumulative drainage area, the number of overflow nodes significantly decreased and the positions were changed, but similar flooding pattern was appeared. However, in the case of more than 6 ha cumulative drainage area, some inundation areas could not be occurred because of deleted nodes from upstream. As a result of comparing flood area and flood depth, it was analyzed that the flood result based on simplification range of 1 ha cumulative drainage area is most similar to the analysis result before simplification. It is expected that this study can be used as reliable data suitable for real-time urban flood forecasting by simplifying sewer network considering SWMM parameters.

• Journal of Korea Water Resources Association
• /
• v.57 no.2
• /
• pp.127-137
• /
• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.