• 한국환경과학회지
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• 제18권1호
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

• 한국지구과학회지
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• 제44권3호
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• pp.185-195
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• 2023

The prolonged and heavy East Asian summer precipitation in 2020 may have been caused by an enhanced Madden-Julian Oscillation (MJO), which requires evaluation using forecast models. We examined the performance of GloSea6, an operational forecast model, in predicting the East Asian summer precipitation during July 2020, and investigated the role of MJO in the extreme rainfall event. Two experiments, CON and EXP, were conducted using different convection schemes, 6A and 5A, respectively to simulate various aspects of MJO. The EXP runs yielded stronger forecasts of East Asian precipitation for July 2020 than the CON runs, probably due to the prominent MJO realization in the former experiment. The stronger MJO created stronger moist southerly winds associated with the western North Pacific subtropical high, which led to increased precipitation. The strengthening of the MJO was found to improve the prediction accuracy of East Asian summer precipitation. However, it is important to note that this study does not discuss the impact of changes in the convection scheme on the modulation of MJO. Further research is needed to understand other factors that could strengthen the MJO and improve the forecast.

• 전기학회논문지
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• 제65권12호
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• pp.2084-2093
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• 2016

It is crucial to predict water temperature for aquatic ecosystem studies and management. In this paper, we first address challenging issues in predicting water temperature in a real time manner and propose a distributed computing model to address such issues. Then, we present an Artificial Neural Network (ANN)-based water temperature prediction model developed for the Soyang River and a cyberinfrastructure system called WT-Agabus to run such prediction models in an automated and real time manner. The ANN model is designed to use only weather forecast data (air temperature and rainfall) that can be obtained by invoking the weather forecasting system at Korea Meteorological Administration (KMA) and therefore can facilitate the automated and real time water temperature prediction. This paper also demonstrates how easily and efficiently the real time prediction can be implemented with the WT-Agabus prototype system.

• 한국지반환경공학회 논문집
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• 제3권4호
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• pp.19-27
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• 2002

하천에서 토사 유실량을 정확하게 예측하는 것은 유량을 예측하는 것만큼 공학적으로 중요한 의미를 지니고 있다. 하천에서 발생하는 토양 유실량은 하천구역내의 수리구조물(댐, 웨어, 방조제 등)의 설계 및 유지관리, 하천개수 및 하도의 안정, 홍수터 관리, 저수지의 설계 및 운영, 항만계획 등 수자원 및 수질의 계획이나 관리에 반드시 고려해야 할 사항이다. 따라서 본 연구에서는 토사 유실량 산정에 가장 일반적으로 쓰이는 RUSLE식을 이용하여 삽교호 유역의 토사 유실량을 모의, 산정하였다. RUSLE식의 매개변수 산정은 GIS 주제도인 경사도와 토지이용도, 토양도로부터 추출, 산정하였으며 이를 작성한 RUSLE 산정프로그램에 적용하였다. 또한 최근 30년치의 유역 강우자료를 바탕으로 빈도별 확률강우량을 산정하여 빈도별 확률강우에 따른 토사 유실량을 산정하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2006년도 학술발표회 논문집
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• pp.1145-1149
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• 2006

The typhoon Rusa passed through the Korean peninsula from the west-southern part to the east-northern part in the summer season of 2002. The flash flood due to the Rusa was occurred over the Korean peninsula and especially the damage was concentrated in Kangnung, Yangyang, Kosung, and Jeongsun areas of Kangwon-Do. Since the latter half of the 1990s the flash flood has became one of the frequently occurred natural disasters in Korea. Flash floods are a significant threat to lives and properties. The government has prepared against the flood disaster with the structural and nonstructural measures such as dams, levees, and flood forecasting systems. However, since the flood forecasting system requires the rainfall observations as the input data of a rainfall-runoff model, it is not a realistic system for the flash flood which is occurred in the small basins with the short travel time of flood flow. Therefore, the flash flood forecasting system should be constructed for providing the realistic alternative plan for the flash flood. To do so, firstly, Flash Flood Monitoring and Prediction (FFMP) Model must be developed suitable to Korea terrain. In this paper, We develop the FFMP model which is based on GIS, Radar techniques and hydro-geomorphologic approaches. We call it the F2MAP model. F2MAP model has three main components (1) radar rainfall estimation module for the Quantitative Precipitation Forecasts (QPF), (2) GIS Module for the Digital terrain analysis, called TOPAZ(Topographic PArametiZation), (3) hydrological module for the estimation of threshold runoff and Flash Flood Guidance(FFG). For the performance test of the model developed in this paper, F2MAP model applied to the Kangwon-Do, Korea, where had a severe damage by the Typhoon Rusa in August, 2002. The result shown that F2MAP model is suitable for the monitoring and the prediction of flash flood.

• Environmental Engineering Research
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• 제15권2호
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• pp.123-126
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• 2010

Modeling non-point pollution across multiple scales has become an important environmental issue. As a more representative and practical approach in quantifying and qualifying surface water, a modular neural network (MNN) was implemented in this study. Two different site-scales ($1.5\;{\times}\;10^5$ and $1.62\;{\times}\;10^6\;m^2$) with the same plants, soils, and paddy field management practices, were selected. Hydrologic data (rainfall, irrigation and surface discharge) and water quality data (time-series nutrient loadings) were continuously monitored and then used for the verification of MNN performance. Correlation coefficients (R) for the results predicted from the networks versus measured values were within the range of 0.41 to 0.95. The small block could be extrapolated to the large field for the rainfall-surface drainage process. Nutrient prediction produced less favorable results due to the complex phenomena of nutrients in the drainage water. However, the feasibility of using MNN to generate improved prediction accuracy was demonstrated if more hydrologic and environmental data are provided. The study findings confirmed the estimation accuracy of the upscaling from a small-segment block to large-scale paddy field, thereby contributing to the establishment of water quality management for sustainable agriculture.

• 한국농공학회지
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• 제26권2호
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Applied Biological Chemistry
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• 제40권4호
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• pp.334-341
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• 1997

강우에 의한 농약의 토양표면 유출특성을 알아보고자 인공강우(20 mm/hr)에 의한 실내 유출실험을 7개 농약을 대상으로 수행한 결과, 유출율은 metolachor 57.0%, alachlor 14.2%, chlorothalonil 13.2%, chlorpyrifos 7.9%, EPN 7.2%, phorate 7.1%, captafol 2.8%였고, 평균 유출농도는 각각 940, 399, 55, 7.0, 9.3, 151, 7.0 ppb였다. 유출율과 농약의 물리화학적 특성(수용성, 분배계수, 토양흡착계수)과의 상관관계를 보면 실내유출실험에서 수용성과의 상관성이 높았으며(r=0.923), 그 이외의 실험조건에서도 수용성 보다 높지는 않았지만 상관성이 유사하였다. 이 결과를 바탕으로 실내 실험결과를 이용한 포장에서의 유출을 예측가능성을 확인하고자 유출을 예측식 $[Y=0.2812{\times}10{\exp}(0.261logWS-0.366)+0.3594{\times}10{\exp}(-0.545logKoc+1.747)+0.3594{\times}10{\exp}(-0.362log\;Kow+1.105]$과 전환식을 도출하였다. 자연포장에서 수행한 captafol의 유출율과 유출율 예측식을 이용하여 계산한 결과를 비교해 보면 예측식에 의한 유출율은 실험치보다 약 6배 이상 높았으나 전환식을 사용시 유출율은 실험치와 유사하였다. 따라서 실내유출 실험을 통한 유출율과 대상농약의 수용성, 분배계수, 토양흡착계수를 사용하여 유도한 유출예측식과 전환식을 이용하여 포장에서 강우에 의한 이들 농약의 유출율 예측이 가능하였다.

• 한국수자원학회논문집
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• 제57권1호
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• pp.35-44
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• 2024

댐유입량 예측에 대하여 데이터 기반 머신러닝 및 딥러닝(Machine Learning & Deep Learning, ML&DL) 분석도구들이 공개되어 다양한 분야에서 ML&DL의 적용연구가 활발히 진행되고 있으며, 모델의 자체 성능향상 뿐만 아니라 모델의 특성을 고려한 데이터의 전처리도 댐유입량을 정확하게 예측하게 하는 중요한 모델성능 향상의 요소라고 할 수 있다. 특히 기존 강우자료는 적설량을 열선 설비를 통하여 녹여 강우량으로 환산되어 있으므로, 융적설에 따른 강우와 유입량의 상관관계를 왜곡하게 된다. 따라서 본연구에서는 소양강댐과 같이 융적설의 영향을 받는 댐유역에 대한 댐일유입량 예측시 겨울에 강설량이 적설이 되어 적게 유출되는 현상과, 봄에 융설로 인하여 무강우나 적은 비에도 많은 유출이 일어나는 물리적 현상을 ML&DL모델로 적용하기 위하여 필요한 강우 데이터의 전처리에 대한 연구를 수행 하였다. 강우계열, 유입량계열을 조합하여 3가지 머신러닝(SVM, RF, LGBM)과 2가지 딥러닝(LSTM, TCN) 모델을 구축하고, 최적 하이퍼파라메터 튜닝을 통하여 적합 모델을 적용하고 한 결과, NSE 0.842~0.894로 높은 수준의 예측성능을 나타내었다. 또한 융적설을 반영한 강우보정 데이터를 만들기 위하여 융적설 모의 알고리즘을 개발하고, 이를 통하여 산정된 보정강우를 머신러닝 및 딥러닝 모델에 적용한 결과 NSE 0.841~0.896 으로 융적설 적용전과 비슷한 높은 수준의 예측 성능을 나타내었으나, 융적설 기간에는 조정된 강우로 학습되어 예측되었을 때 실측유입량에 근접하는 모의결과를 나타내었다. 결론적으로, 융적설이 영향을 미치는 유역에서의 데이터 모델 적용시에는 입력자료 구축시 적설 및 융설이 물리적으로 타당한 강우-유출 반응에 적합하도록 전처리과정이 중요함을 밝혔다.