• Journal of Environmental Science International
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• v.15 no.2
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• pp.141-155
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• 2006

This study is aimed at the development of a runoff forecasting model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting, The study area is the downstream of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model. The model performance was improved as the measuring time interval$(T_m)$ was smaller than the sampling time interval$(T_s)$. The Neuro-Fuzzy(NF) and TANK models can give more accurate runoff forecasts up to 4 hours ahead than the Feed Forward Multilayer Neural Network(FFNN) model in standard above the Determination coefficient$(R^2)$ 0.7.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.439-444
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• 2006

Korea Peninsula consists of approximately 70% of mountainous terrain of total area, in addition, annual average rainfall is plentiful, especially during rainy season of summer, and it is often accompanied with typhoon and heavy rain, which results in frequent landslides. Since there are limitations with existing methods to analyze extensive disasters, it is necessary to develop new remote sensing technology using an artificial satellite to study on landslides closely. This paper is written in order to establish the database with map information on various landslides using GIS, furthermore, to analyze precariousness of the areas, which are susceptible to landslide, and risks of potential areas in consideration of heavy rain, based on land-cover classification derived from images from satellite.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.72-76
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• 2011

본 연구는 레이더 강우 영상에서 추출된 강우 패턴을 인공신경망으로 처리하여 단기 강우 예측을 수행하는 방안을 제시한 것이다. 본 연구에 활용한 CAPPI 영상자료로는 편차 보정과 품질 관리가 이루어지고 있으며 획득이 용이한 기상청 자료를 이용하였으며 CAPPI의 PNG 영상으로부터 강우 패턴을 추출하고, 이를 역전파 알고리즘의 인공신경망 강우 예측 모형에 학습시켜 단기 강우를 예측하기 위한 절차를 제시하였다. 이를 위하여 강우의 시공간적 변화 패턴 추출을 위한 영상 처리와 GIS 자료처리 기법을 제시하였고 이를 인공신경망의 단기 강우 예측 학습과 검증에 적용하여 본 연구에서 제시된 기법의 타당성을 검토하였다.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.9
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• pp.464-469
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• 2004

The leakage current of transmission insulators contaminated with salt, clay, and kaolin was examined in the Gochang's Long Periods Testing Center. The Insulators were artificially contaminated and estimated with the method of equivalent salt deposit density(ESDD). The artificially contaminated insulators were installed with the same condition as in the real transmission power line and applied with 154 (kV). The leakage current of the artificially contaminated insulators was measured with environment conditions, such as temperature and humidity by the a automatic leakage current detecting system. The leakage current of heavily contaminated insulator was abruptly increased above 72［%］ of humidity, even though the leakage current was similar between the contaminated and non-contaminated insulators below 72［%］ of humidity. Also, it was found that the humidity was much more important than the temperature in the leakage current of transmission insulators. The leakage current of contaminated insulator was decreased when it was plenty of rainfall, resulting from natural washing.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.2
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• pp.207-214
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• 2013

To evaluate characteristics of water quality in Jeju, a study was implemented for Han stream and retention ponds. Inflow water quality of retention ponds was heavily dependent on precedent rainfall, and no pesticide was detected due to the little artificial pollution sources. A smooth settling efficiency curve was noticed because heavy particles were already settled down in front of the retention pond. There were weak relationships between retention time and water quality, and this can be attributable to high concentration of pollutants influx at peak inflow. In addition, as Han stream retention pond has a role of groundwater recharge, inflow control based on water quality as well as quantity is needed to maximize pollutant removal at the retention ponds.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05b
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• pp.65-68
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• 2004

This paper researched leakage current characteristics of artificially contaminated EHV insulators through construction of long-term testing facility. Insulators were contaminated and classified into the ESDD contaminated levels under IEC standards method. As the test results of contaminated insulators was carried out several experiments, leakage current greatly increased during initial rainfall. After contaminated insulators were naturally washed by rain, leakage current was not increased.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.310-310
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• 2012

확률강우량 산정은 수공구조물의 설계에 있어서 중요한 과정이다. 확률강우량을 산정함에 있어 지난 수십년간 모멘트법, 최우도법, 확률가중모멘트법, 그리고 L-모멘트법 등의 매개변수적 방법이 발달되어 적용되어 왔다. 매개변수적 빈도해석 방법은 그 적용성이 여러 연구를 통해 검정되었지만 가정한 확률분포와 매개변수 추정방법에 따라 확률강우량이 달라지며 강우지속시간과 기후변화 등에 따른 분포의 변동성을 고려해야 하는 단점이 있다. 매개변수적 빈도해석 방법의 단점을 극복하기 위하여 최근에 핵밀도함수 등을 포함한 다양한 비매개변수적 빈도해석 방법이 제안되고 있다. 본 연구에서는 서울기상관측소의 지난 50년간 지속시간 24시간 강우량을 바탕으로 수자원 분야에서 다양하게 적용된 바가 있는 인공신경망 기법과 대표적인 매개변수적 빈도해석 방법인 L-모멘트법을 이용하여 확률강우량을 산정하고 비교하였다. 그 결과 인공신경망 기법은 전통적인 매개변수방법의 하나인 L-모멘트법 보다 확률강우량 산정에 있어서 높은 정확도를 가지는 것으로 나타났다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1166-1170
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• 2004

The leakage currents of transmission insulator were investigated as a function of environmental conditions, such as temperature, humidity, and rainfall. The insulators were artificially contaminated with insoluble yellow soil and kaolin which helped salt to stick on the surface of insulator. The insulators contaminated with the grade of B, C, and D were installed in the KoChang Testing Center. The leakage currents were measured and compared with non-contaminated insulators. The results indicated that the most important factor affecting leakage current was humidity. After heavy rain, the artificially contaminated salt was dissolved, resulting in similar characteristics between with and without contamination

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.35-35
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• 2022

최근에 딥 러닝(Deep learning) 기반의 많은 방법들이 수문학적 모형 및 예측에서 의미있는 결과를 보여주고 있지만 더 많은 연구가 요구되고 있다. 본 연구에서는 수자원의 가장 대표적인 모델링 구조인 강우유출의 관계의 규명에 대한 모형을 Long Short-Term Memory (LSTM) 기반의 변형 된 방법으로 제시하고자 한다. 구체적으로 본 연구에서는 반응변수인 유출량에 대한 직접적인 고려가 아니라 그의 1차 도함수 (First derivative)로 정의되는 Delta기반으로 모형을 구축하였다. 또한, Attention 메카니즘 기반의 모형을 사용함으로써 강우유출의 관계의 규명에 있어 정확성을 향상시키고자 하였다. 마지막으로 확률 기반의 예측를 생성하고 이에 대한 불확실성의 고려를 위하여 Denisty 기반의 모형을 포함시켰고 이를 통하여 Epistemic uncertainty와 Aleatory uncertainty에 대한 상대적 정량화를 수행하였다. 본 연구에서 제시되는 모형의 효용성 및 적용성을 평가하기 위하여 미국 전역에 위치하는 총 507개의 유역의 일별 데이터를 기반으로 모형을 평가하였다. 결과적으로 본 연구에서 제시한 모형이 기존의 대표적인 딥 러닝 기반의 모형인 LSTM 모형과 비교하였을 때 높은 정확성뿐만 아니라 불확실성의 표현과 정량화에 대한 유용한 것으로 확인되었다.