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교각 주변의 저수류 (低水流) 흐름 변화에 대한 2차원 분석
연인성,이재경,연규방,Yeon. In-Sung,Lee. Jai-Kyung,Yeon. Gyu-Bang 한국방재학회 2009 한국방재학회논문집 Vol.9 No.4
하천을 횡단하는 구조물들은 흐름을 변화시킬 뿐 아니라, 산지 및 중소하천에서 유송잡물로 인한 수위 상승의 원인을 제공하기도 한다. 이번 연구에서는 유속 및 경사 등의 하천특성인자와 교각의 영향으로 변화되는 흐름을 2차원 모형으로 분석하였다. 또한 강우로 인해 변화된 유량이 흐름에 미치는 영향도 비교하였다. 하도내의 유속은 SMS (Surface water Modeling System) 및 RMA2 모형을 통해서 분석할 수 있었으며, 경사가 크고, 하도의 폭이 좁은 구간에서 비교적 유속이 큰 것으로 나타났다. 또한 교각 주변에서는 교각과 교각 사이의 중심부에서 유속이 가장 크게 나타났다. 강우로 인해 증가된 유량은 유속을 증가시키고 흐름 분포를 현저하게 변화시키는 것을 확인할 수 있었다. The flow is changed by the structure which goes across the river. The structure with debris causes high water level and overflow. The changed flow, which caused by pier and stream characteristics like velocity and slope, was analysed by 2D model. After rainfall, the influences of increased discharge were evaluated. Velocity was simulated in the channel by SMS (Surface water Modeling System) using RMA2, and high velocity values were found in the steep and narrow reach. Highest velocity value around piers was showed in the middle of space between two piers. The increased discharge due to rainfall increases velocity and changes flow contour considerably.
연인성(Yeon In Sung),이재경(Lee Jai Kyung) 대한토목학회 2007 대한토목학회논문집 B Vol.27 No.6B
국내에서는 주요 하천에 수질자동측정망이 설치되어 운영되고 있다. 적절한 경보를 위해서 실시간 감시체제가 구축되어야 할 것이며, 이를 위해 다양한 기법이 검토되어야 할 것이다. 이번 연구에서는 다중퍼셉트론과 BAM을 이용하여 안정, 주의, 경고 상태를 학습하기위해 3가지 기준축을 적용하였다. 수질오염 사고에 대한 시나리오를 작성하여 BAM의 적용성을 검토하였으며, BAM은 이상 수질에 대한 위험 상태를 적합하게 구별하는 것으로 나타났다. BAM은 다중퍼셉트론과 비교하여 만족할 만한 결과를 보였으며, 9×9의 기준축은 BAM의 학습과 이상 수질을 판별하는데 가장 효과적이었다. The automatic water quality monitoring networks are operated in Korea. It needs to construct the real-time warning system for water quality monitoring. To do this, a variety of method must be estimated. In this paper, three standard axes were applied to train stability, notice, and warning situation using multi-perceptron and BAM (Bidirectional Associative Memory). The application capability of BAM was estimated by the scenario of pollution accident. As a results, it was verified that BAM can reasonably judge the noized water pollution data. The estimation results of BAM showed better than those of multi-perceptron. The developed 9×9 standard axis was most useful for the judgement of water pollution and training BAM.
연속 측정된 대청호 Chlorophyll-α의 자료 특성 및 상관 분석
연인성 ( In Sung Yeon ),홍지영 ( Ji Young Hong ),홍은영 ( Eun Young Hong ),임병진 ( Byung Jin Lim ) 한국물환경학회 2010 한국물환경학회지 Vol.26 No.6
The toxin of Cyanobacteria (blue-green algae) during summer season has been a problem and early prevention should be considered. A variety of methods can be used to forecast algal blooms and this study aims at examining feasibility of chlorophyll-α. The real-time data were collected by automatic water quality monitoring system (AWQMS) in Daecheong reservoir and invalid data were sorted by experts. And then, the sorted data were filled using linear interpolation. When the concentration of chlorophyll-α increased by 15 mg/m(3). water temperature and pH exceeded 26.8℃ and 9.5 respectively. As a result of correlation between chlorophyll-α and other parameters(i.e. water quality items and hydrological data), temperature (r=0.502-0.574). pH (r=0.583-0.681), total organic carbon (TOC, r=0.583-0.681) comparably had higher values. Meanwhile, the data around a day or two showed the highest correlation. In addition, chlorophyll-α is considered to be significantly effected by precipitation and inflow.
호소내 Chl-a의 일단위 예측을 위한 신경망 모형의 적정 파라미터 평가
연인성 ( In Sung Yeon ),홍지영 ( Ji Young Hong ),문현생 ( Hyun Saing Mun ) 한국물환경학회 2011 한국물환경학회지 Vol.27 No.4
Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.
가상하도 내에서 2차원 흐름분석을 통한 오염원의 유입 지점 탐색
연인성 ( In Sung Yeon ),조용진 ( Yong Jin Cho ) 한국물환경학회 2011 한국물환경학회지 Vol.27 No.1
2D pollutant transport model was applied to the simulation of contaminant transport in the channel. At first, two kinds of virtual channels having different slopes were designed. The distribution of contaminant, which flows from one of the three drainages to the main channel, was simulated by each 2D model. Concentrations of 745 nodes were converted to input data of neural network model (Multi-perceptron) for training and verification using matrix. The first three cases (Case A-1, A-2, A-3) were used for training Multi-perceptron, the other three cases (Case B-1, B-2, B-3) were used for verification. As a result, Multi-perceptron reasonably divided the cases into the three characteristics which have different contaminant distributions due to the different input point of water pollution source. It can be a useful methodology for the water quality monitoring and backtracking.