• 한국농공학회지
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• 제24권1호
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• pp.53-62
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• 1982

This study was attempted to find best fitted distribution and the equations for probable maximum flow with the evaluation of parameters by the method of moment for the rat- ional design of hydraulic structures in the annual exceedance series. Six subwatersheds were selected as studying basins along Geum River basin. The results obtained through this study were analyzed and summarized as follows. 1. Fitted probability distribution was showed in the order of Three Parameter Lognorm al, Type 1 Extremal, Exponential, Pearson Type III, and Log Pearson Type I distribu- tion as the results of x$^2$ goodness of fit test. 2. Kolmogorov-Smirnov test showed in the order of Three Parameter Lognormal, Exp- onential' Pearson Type III, Log Pearson Type III and Type 1 Extremal distribution for the fitted probability distribution. 3. It can be concluded that Three parameter Lognormal distribution is a best fitted one among some other distributions out of respect for each both tests. An Exponential distribution was proposed as a suitable one by Chow, V.T. showeci lower fittness than that of Three Parameter Lognormal in Geum River basin. 5. Probable flood flow equations followins the return periods for each station were obt- ained by Three Parameter Lognormal distribution. 6. It is urgently essential that best fitted probability distribution should be established for the annual exceedance series in the main river systems of Korea.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2010년도 학술발표회
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• pp.283-287
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• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.

• 한국수자원학회논문집
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• 제56권9호
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• pp.563-574
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• 2023

최근 기후변화로 인해 유역의 기상자료에 대한 반응이 달라지고 있어 강우-유출 모의에 대한 연구는 중요해지고 있다. 아울러 최근 기계학습 기법에 대한 높은 관심으로 이를 통한 강우-유출 모의 역시 활발하게 증가하고 있으나 기계학습 모형이 전통적으로 사용되어온 개념적 모형에 비해 활용성이 높은지는 아직 확실치 않다. 본 연구에서는 개념적 모형인 GR6J와 기계학습 모형인 Random Forest 성능을 한국 전역의 38개 계측 유역에 대해 계측 유역 예측기법과 미계측 유역 예측기법을 이용해 평가하였다. 먼저 계측 유역 적용기법 평가를 위해 각 모형을 관측 일 유량자료에 학습시키고 분리된 평가기간에 대한 모의성능을 비교하였다. 이후 미계측 유역 모의성능 평가를 위해 인접성 기반 지역화 방법을 Leave-One-Out Cross-Validation (LOOCV)을 이용해 평가하였다. 그 결과 계측 유역 평가에서는 Random Forest 기법이 GR6J 모형보다 일관되게 높은 성능을 보였다. 학습된 데이터를 출력 값으로 재생산하도록 구조화되어 있는 기계학습 기법이 개념적 이론을 통한 모형보다 높은 재현성을 갖기 때문으로 판단된다. 하지만 Random Forest 모형의 성능은 미계측 유역의 예측기법으로는 재현되지 않았고 GR6J 모형보다 성능이 더 낮은 것이 확인되었다. 본 연구는 기계학습 모형은 계측 유역의 유출예측에는 적용성이 높을 수 있으나 미계측 유역에 대한 적용가능성은 전통적인 개념적 모형보다 낮을 수 있음을 제시한다.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2002년도 총회 및 춘계학술발표회
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• pp.237-242
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• 2002

This study is objected to assess the recharges of phreatic aquifers in the south Korea. The water level data of the national ground-water monitoring network were analysed by PCA(Principal Component Analysis), and classified to 8 types. The recharge were estimated by ‘water-level change method’ on basis of the classified types and compared with the previous methods(hydrograph separation methods) on basis of 4 river basins. The recharge were various type by type and site by site. But the recharge estimated by this study were consistent with that of the other studies.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2001년도 학술발표회 발표논문집
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• pp.89-92
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• 2001

This study was to analyse the correlations between pumping rate to irrigated area and rainfall amount in the Geum river basins. A total of 84 pumping stations and field data from the paddy of 28,772 ha were introduced to the analysis. The results showed that the pumping volume was highly correlated to the rainfall during the irrigation period and irrigated area. But, it was difficult to determine the exact correlation factors, because of the lack of data like the efficiency of water in the paddy field.

• 대한지리학회:학술대회논문집
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• 대한지리학회 2004년도 춘계학술대회
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• pp.55-55
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• 2004

북한은 70년대 후반 들어 더욱 심각해진 식량문제를 해결하기 위해 개별 농가의 텃밭이나 뙈기밭, 기업이나 공장에서 운영하는 부업밭 개간을 권장하였다 이러한 농지개간은 90년대의 자연재해로 인한 식량난이 악화되자 산지와 간석지로 확대되기 시작하였다. 특히 산지개간의 경우 고도가 높아지고 급경사지로 개간이 진행됨에 따라 이로 인한 지표침식 문제들은 최근의 현지를 방문한 인사들이나 인공위성 영상을 통해서 직ㆍ간접적으로 알려지거나 파악되고 있다. (중략)

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.1073-1080
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• 1996

The impact of the input and output probability distributions on the performance of neural networks to forecast two year peak stream flow (cubic meters per second) is examined for two major river basins of the US. The neural network input consisted of drainage area(square kilometers ) and elevation (meters). When data are normally distributed , the neural networks predict much better than when the data are non-normal and have larger tails in their distributions.

• 한국수자원학회논문집
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• 제34권2호
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• pp.119-130
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• 2001

본 연구에서는 한강유역의 1일, 2일, 3일 연최대강우자료를 대상으로 L-모멘트법을 이용한 지점 빈도해석과 지역 빈도해석을 실시하여 그 결과를 비교하였다. 지역빈도해석을 실시하기 위하여 한강유역을 남한강, 북한강, 한강하류부 유역의 3개 소유역으로 분할하고, 각 유역에 대한 자료의 이산도 및 동질성을 검토하였으며, 각 소유역에 대하여 여러 분포형을 적용한 결과, 남한강유역과 한강하류부 유역은 lognormal 분포형, 북한강 유역은 gamma-3 분포형이 적정분포형으로 선정되었다. 지역빈도해석과 지점빈도해석을 통하여 선정된 확률분포형을 이용, Monte Carlo 모의를 수행하였으며, 재현기간에 따른 상대편의와 상대제곱근 오차를 산정하였다. 지역빈도해석과 지점빈도해석을 비교한 결과 상대제곱근오차에 있어서 지역빈도해석을 수행한 경우가 지점빈도해석에 비해 그 결과가 우수하였으며, 재현기간이 커질수록 그 차이는 현저하게 나타났다. 따라서, 한강유역의 강우량에 대해서 지역빈도해석 수행함이 지점빈도해석에 비해 우수하다는 결론을 얻게 되었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• 한국농공학회:학술대회논문집
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• 한국농공학회 1998년도 학술발표회 발표논문집
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• pp.269-278
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• 1998

The Planning characteristics of the canals linking river basins and estuary reservoirs have been reviewed in relation to the trend widening the areas of water resources planning. It has been also tried to justify the characteristics of the link canals in the history of irrigation development. It was concluded that the link canals were resulted from the most recent technology as far as irrigation systems were concerned, why these were long- and large-scale multipurpose canals ； the hydraulic heads of the link canals could be controlled easily ； and which consisted of open channels, pipelines and control reservoirs. The types and general characteristics of the canals have been reviewed, and the fundamental concept and characteristics in the planning of the canals have been described.