• 환경영향평가
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• 제9권3호
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• pp.163-176
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• 2000

The purpose of this study is to analyze the pollutant loads and its distribution, and to suggest the management of nonpoint sources in Daechong Reservoir. The loads from point and nonpoint sources such as population, industry, livestock and land use were calculated per stream or river with topography(1:25,000) of the watershed of Daechong Reservoir. The generating pollutant loads were obtained through multiplication of pollutant sources by generating pollutant quantity per unit pollutant source. The effluent point sources loads is defined as loads from wastewater treatment facilities such as domestic, industrial and livestock wastewater treatment facilities, which were calculated through multiplication of effluent flowrates by water quality constituents concentration. Untreated point sources loads were estimated to be 35 % of total point sources loads. The effluent nonpoint sources pollutant loads were obtained through the multiplication of generating nonpoint sources loads by effluent ratios based on previous studies. The effluent nonpoint sources loads have the ratio of 26.2% of total BOD effluent loadings, 20.1% of total T-N effluent loadings, and 10.5% of total T-P effluent loadings. For the reduction of nonpoint sources loads in Daechong Reservoir, silviculture, artificial wet land, and grassed waterways could be applied. And untreated livestock waste scattered can result in nonpoint loadings, so required the livestock wastes treatment facilities and purifying facilities together with the management of shed, pasture, livestock waste storage site and composting site. Finally, remote sensing and GIS should be applied to the identification of distribution of water quality, watershed, the location and scale of nonpoint sources, effluent process during rainfall, for more detailed analysis of nonpoint sources.

• 환경영향평가
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• 제8권1호
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• pp.93-105
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• 1999

Lately water quality of Daechong Reservoir has become more eutrophicated than ever before and there has been much concern over especially the eutrophication of the embayment near Daejon and Chongju Water Intake Tower every summer. The purpose of this study is to predict the impact of change in the pollutant loading, flowrate, nitrogen and phosphorus release from sediment, SOD(sediment oxygen demand) upon the water quality of Daechong Reservoir by WASP5/EUTR05 in order to suggest water quality management alternatives. The data of Sep. 1995 were used for the calibration of the model and those of Sep. 1997 was for verification. The result of the modeling can be summarized as follows. 1. The 50% increase(decrease) of pollutant loading has caused that of T-N concentration by 0.10-0.14 mg/l, T-P concentration by 0.003-0.005 mg/l, and CBOD concentration by 0.16-0.18 mg/l. But the ratio of DO change by the change of pollutant loading was relatively small. 2. The sensitivity test of NH4 flux to T-N and that of P04 flux to T-P shows that T-N and T-P concentration were changed more in the epilimnion segments (SEG4, SEG5, SEG6, SEG7) than the other segments. As SOD increases, DO was predicted to decrease more especially in the hypolimnion (SEG9-SEG14). 3. As flowrate increase, the concentration of T-N, T-P, and CBOD were predicted to decrease, but DO concentration increased especially in the hypolimnion segments(SEG11, SEG12, SEG13, and SEG14). As the flowrate changed from $119m^3/sec$ to $50m^3/sec$, the concentration of T-N and CBOD in the hypolimnion was predicted to decrease.

• 환경영향평가
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• 제3권2호
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• pp.33-45
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• 1994

Water quality of Chongju and Daejeon Water Intake Tower Region, embayments in Daechong Reservoir was found to be worse than that of main lake after analysis of water which were sampled during April, July, October in 1993. Concentration of COD and SS at those two water intake tower sites were 2.8-5.6 mg/l and 2.2-3.2 mg/l, higher than that of main lake. T-N concentration of those two sites was 1.1-1.9 mg/l similar to that of main lake, and T-P concentration of those two sites was 0.14-0.18 mg/l, higher than that of main lake. This study used water quality model of embayment which can analyse pollutant loads from stream and surrounding land use, advection, decay, and diffusion transport between embayment and main lake. The model can predict water quality of embayment according to the change of pollutant load, water elevation of embayment, quantity of water intake in order to suggest water quality management. This study suggests embayment water quality management alternatives, 1) construction of waste water treatment facilities at embayment and main lake for the decrease of pollutant loading, 2) water intake at main lake less polluted or eutrophicated than embayment, and 3) outflow elevation selection for polluted hypolimnion water outflow during stratification.

• 한국환경생물학회:학술대회논문집
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• 한국환경생물학회 2004년도 학술대회
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• pp.42-42
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• 2004

• 환경영향평가
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• 제11권1호
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• pp.1-12
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• 2002

중부지방 3백만 주민의 상수원으로 이용되는 대청호는 부영양화가 심화되어 적절한 수질관리가 없으면 상수원수의 최저등급인 3급수를 만족할 수 없는 상태에 이르고 있다. 그래서 2002년 1월에 금강수계 물관리 및 주민지원 등에 관한 법률이 제정되었는 바, 오염총량제와 수변구역의 지정을 통한 수질보전 등을 주요한 내용으로 포함하고 있다. 오염총량제를 실천하기 위해서는 대청호의 환경용량을 추정하는 것이 필수 불가결하다. 환경용량의 추정은 장래 대청호 수질영향권역에서 개발사업이 이뤄질 때 환경영향평가 특히 누적영향평가를 위해 필요하다. 대청호의 환경용량을 추정하기 위해 오염부하량과 유량의 변화가 대청호의 총질소와 총인농도에 미치는 영향을 WASF5모형으로 분석하였다. 총질소 부하량이 50%증가한다고 가정했을때 갈수기. 명수기, 홍수기에 각각 0.14-0.29 mg/l, 0.12-0.16 mg/l and 0.03-0.05 mg/l 정도의 농도 변화가 예측되었다. 총질소 부하량이 지금의 반이하로 감소하더라도 총질소농도 5급수기준 (1.0-1.5 mg/l)을 달성할 수가 없다. 대청호의 환경용량을 상수원수 3급수기준으로 산정한다면, 그것은 현재의 총질소 부하량의 반보다 훨씬 적다고 추정할 수 있으며 따라서 총질소 부하량은 지금의 반이하로 줄어들어야 할 것이다. 총인 부하량이 50%증가한다고 가정했을 때 갈수기, 평수기, 홍수기에 각각 0.005-0.011 mg/l, 0.004-0.006 mg/l and 0.001-0.002 mg/l 정도의 농도 변화가 예측되었다. 총인 부하량이 지금의 반이하로 감소하더라도 총인 농도 4급수기준 (0.05-0.1 mg/l)을 간신히 달성할 수 있다. 따라서 대청호의 환경용량은 지금의 총인 부하량의 반정도로 추정할 수 있으며 총인 부하량은 지금의 반이하로 줄어들어야 할 것이다. 오염부하량을 줄이기 위한 방안을 정오염원과 비정오염원으로 나눠 제시하였고 오염총량제 실현방안을 제시하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 1990년도 수공학논총 제32권
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• pp.105-116
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• 1990

홍수기간중에는 강우, 유출, 저수지 및 하류 수위등의 수시로 변하는 자료들을 on-line system으로 읽어서 실시간으로 저수지를 운영하므로써 현상태에서의 최선의 방류량을 결정하는 것이 가장 바람직하며, 이를 위한 선행조건으로 정확한 강우및 유출예측모형과 하류의 홍수추적모형이 연계 된다면 저수지의 홍수조절 용량을 최대한 활용하여 최적의 방류량을 결정할 수 있다. 본 연구에서는 홍수기중 실시간 저수지 운영을 위해 여러가지 제약조건을 고려한 모델을 개발하여, 단일 저수지인 대청 다목적 댐을 대상으로 적용하였다. 예측 유입량 대신에 재현기간별 홍수수문곡선과 실제의 저수지 유입량을 이용했으며, 하류의 홍수추적 모형은 일정한 유량을 방류하는 것이 가장 안전하다고 가정하여 최대 방류량을 변화시키므로써 하도 추적모형을 대신 하였다. 본 모델은 별도의 강우 유출예측모형과 하도의 홍수추적모형과 더불어 종합적인 실시간 저수지 운영모델이 된다.

• 한국동물위생학회지
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• 제15권2호
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• pp.203-214
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• 1992

Livestock waste causes ground and surface water pollution, eutrophication of reservoir as well as adverse affects living environment of those who dwell nearby. In order to investigate the pollution load from livestock waste, physical and chemical characteristics of the waste were determinded in the survey of livestock farms. Once pollution load is obtained as a function of the origin units of livestock, the influence of livestock waste on the Daecheong reservoir was studied. 1. For Daecheong reservoir inflow area, the liverstock manure productions of beef cattle swine, dairy cattle and chicken were 1,135.6t/day, 480t/day, 241.3t/day, 48t/day respectively； Beef cattle was the mai or source of the pollution. Pollution loading productions due to the livestock waste around the were found to be 53.31t/day for BOD, 222.49t /day for COD, 261.99t/day for T-S, 9.64t/day for T-N, 6.54t/day for T-P 2. Bocheong stream turnd out to be the major contribution for pollution loading production to Daecheong reservoir with 10,748kg /day of BOD, 47,157kg /day of COD, 1,946kg /day of T-N, 1,271kg /day of T-p. 3. Actual pollution loadings from livestock wastes for the area of Daecheong reservoir were estimated as 1,997kg /day(BOD), 8,546kg /day(COD), 364kg /day(T-N), 243kg /day (T-P), respectively Therefore, advanced treatment for livestock waste is recommended for Daectleong reservoir inflow area to eliminate the nutrients which are major sources of eutrophication of the reservoir.

• 한국조경학회지
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• 제18권4호
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• pp.101-115
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• 1991

A study on the method of evaluation the urban amenity structure in Pusan city was established. Finally a survey sites out of 41 regions were selected on the basis of questionnaires : Taejong-dae and Haeun-dae as a seascape, Pumosa and Daesin-park as a mountain, Daechong-park and Seongjigok-park as a mixed, and Chungryulsa, Yongdoosan-park and U. N. Cemetry as a urban type. The abstracted results of amenity elements were revealed as natural environments including convex type as beach, reservoir, valley and mountain, and plant elements including woods and flower beds which raised amenity. The elements of social surroundings including children's playing, the aged's rest, and elements of structures including historic and memorial structures and high buildings. Amenity element made up of each space by region were abstracted from the Semantic Differential method. According to the factor analysis on the ground SD scale values, Kaiser's measure of sampling adequacy for 24 variables is 08602 and very high. Four factors including pleasantness, healthiness, convenience and safety showed 54.42 percent for total variance. By means of multiple regression, the model was as follows : Y=1.6636+0.3684X4+0.1955X11+0.1614X15-0.1688X23+0.1468X24. Therefore, Y:amenity, X4:beautiful-ugly, X11:clean-dirty, X15:creative-imitative, X23:cozy-dreary, X24:free-restrained. All variables in the model were significant at 0.001 level. According to the results of regression on satisfaction, the variables of satisfaction affecting amenity are the size of green space, the condition of management and the harmony with the surroundings. I think the considerating on the above could improve amenity of each region and further Pusan city.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.43-43
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• 2022

유해 조류 대발생은 전국 각지의 인공호소나 하천에서 다발적으로 발생하며, 경관을 해치고 수질을 오염시키는 등 수자원에 부정적인 영향을 미친다. 본 연구에서는 인공호소에서 발생하는 유해 조류 대발생을 예측하기 위해 심층학습 기법을 이용하여 예측 모델을 개발하고자 하였다. 대상 지점은 대청호의 추동 지점으로 선정하였다. 대청호는 금강유역 중류에 위치한 댐으로, 약 150만명에 달하는 급수 인구수를 유지 중이기에 유해 남조 대발생 관리가 매우 중요한 장소이다. 학습용 데이터 구축은 대청호의 2011년 1월부터 2019년 12월까지 측정된 수질, 기상, 수문 자료를 입력 자료를 이용하였다. 수질 예측 모델의 구조는 다중 레이어 퍼셉트론(Multiple Layer Perceptron; MLP)으로, 입력과 한 개 이상의 은닉층, 그리고 출력층으로 구성된 인공신경망이다. 본 연구에서는 인공신경망의 은닉층 개수(1~3개)와 각각의 레이어에 적용되는 은닉 노드 개수(11~30개), 활성함수 5종(Linear, sigmoid, hyperbolic tangent, Rectified Linear Unit, Exponential Linear Unit)을 각각 하이퍼파라미터로 정하고, 모델의 성능을 최대로 발휘할 수 있는 조건을 찾고자 하였다. 하이퍼파라미터 최적화 도구는 Tensorflow에서 배포하는 Keras Tuner를 사용하였다. 모델은 총 3000 학습 epoch 가 진행되는 동안 최적의 가중치를 계산하도록 설계하였고, 이 결과를 매 반복마다 저장장치에 기록하였다. 모델 성능의 타당성은 예측과 실측 데이터 간의 상관관계를 R2, NSE, RMSE를 통해 산출하여 검증하였다. 모델 최적화 결과, 적합한 하이퍼파라미터는 최적화 횟수 총 300회에서 256 번째 반복 결과인 은닉층 개수 3개, 은닉 노드 수 각각 25개, 22개, 14개가 가장 적합하였고, 이에 따른 활성함수는 ELU, ReLU, Hyperbolic tangent, Linear 순서대로 사용되었다. 최적화된 하이퍼파라미터를 이용하여 모델 학습 및 검증을 수행한 결과, R2는 학습 0.68, 검증 0.61이었고 NSE는 학습 0.85, 검증 0.81, RMSE는 학습 0.82, 검증 0.92로 나타났다.