• 한국지하수토양환경학회지:지하수토양환경
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• 제21권6호
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• pp.22-35
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• 2016

Global climate change could have an impact on hydrological process of a watershed and result in problems with future water supply by influencing the recharge process into the aquifer. This study aims to assess the change of groundwater recharge rate by climate change and to predict the sustainability of groundwater resource in Pyoseon watershed, Jeju Island. For the prediction, the groundwater recharge rate of the study area was estimated based on two future climate scenarios (RCP 4.5, RCP 8.5) by using the Soil Water Balance (SWB) computer code. The calculated groundwater recharge rate was used for groundwater flow simulation and the change of groundwater level according to the climate change was predicted using a numerical simulation program (FEFLOW 6.1). The average recharge rate from 2020 to 2100 was predicted to decrease by 10~12% compared to the current situation (1990~2015) while the evapotranspiration and the direct runoff rate would increase at both climate scenarios. The decrease in groundwater recharge rate due to the climate change results in the decline of groundwater level. In some monitoring wells, the predicted mean groundwater level at the year of the lowest water level was estimated to be lower by 60~70 m than the current situation. The model also predicted that temporal fluctuation of groundwater recharge, runoff and evapotranspiration would become more severe as a result of climate change, making the sustainable management of water resource more challenging in the future. Our study results demonstrate that the future availability of water resources highly depends on climate change. Thus, intensive studies on climate changes and water resources should be performed based on the sufficient data, advanced climate change scenarios, and improved modeling methodology.

• 한국방재학회 논문집
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• 제10권2호
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• pp.111-122
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• 2010

한강의 잠수교와 둔치는 장마철에 큰 홍수가 발생하면 침수가 되기 때문에 시민들의 안전과 편의를 위해 홍수로 인한 침수 발생시간을 예측하는 것은 대단히 중요하다. 본 연구에서는 FLDWAV모형을 이용하여 한강하류부의 팔당댐 방류량과 황해(서해) 조석이 한강하류부 수위에 미치는 영향을 분석하였다. 연구 대상구간은 팔당댐 하류부에서 전류지점까지이며, 조석영향을 고려하기위해 하류경계조건인 전류수위는 팔당댐방류량과 인천조위를 이용한 다중선형회귀분석을 통해 산정된 예측 전류수위를 사용하였다. 본 연구에서는 잠수교와 주요 둔치에서 수위상승도달시간을 산정하였고, 팔당댐 방류유형과 황해조석에 따른 수위상승도달시간을 팔당댐 방류량의 함수인 2차다항식으로 나타냈다.

• 한국물환경학회지
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• 제39권1호
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• pp.1-8
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• 2023

Monitoring and prediction of water quality are essential for effective river pollution prevention and water quality management. In this study, a multi-classification model was developed to predict chlorophyll-a (Chl-a) level in rivers. A model was developed using CatBoost, a novel ensemble machine learning algorithm. The model was developed using hourly field monitoring data collected from January 1 to December 31, 2015. For model development, chl-a was classified into class 1 (Chl-a≤10 ㎍/L), class 2 (10<Chl-a≤50 ㎍/L), and class 3 (Chl-a>50 ㎍/L), where the number of data used for the model training were 27,192, 11,031, and 511, respectively. The macro averages of precision, recall, and F1-score for the three classes were 0.58, 0.58, and 0.58, respectively, while the weighted averages were 0.89, 0.90, and 0.89, for precision, recall, and F1-score, respectively. The model showed relatively poor performance for class 3 where the number of observations was much smaller compared to the other two classes. The imbalance of data distribution among the three classes was resolved by using the synthetic minority over-sampling technique (SMOTE) algorithm, where the number of data used for model training was evenly distributed as 26,868 for each class. The model performance was improved with the macro averages of precision, rcall, and F1-score of the three classes as 0.58, 0.70, and 0.59, respectively, while the weighted averages were 0.88, 0.84, and 0.86 after SMOTE application.

• 한국물환경학회지
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• 제23권5호
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• pp.591-599
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• 2007

The quality of water released from a stratified reservoir is dependent on various factors such as the location and shape of intake facility, structure of reservoir stratification, profile of water quality constituent, and withdrawal flux. Sometimes, selective withdrawal capabilities can provide the operational flexibility to meet the water quality demands both in-reservoir and downstream. The objective of this study was to evaluate the performance of a one-dimensional reservoir selective withdrawal model (SELECT) as a tool for supporting downstream water quality management for Daecheong and Imha reservoirs. The simulated water quality variables including water temperature, dissolved oxygen (DO), conductivity, turbidity were compared with the field data measured in tailwater. The model showed fairly satisfactory results and high reliability in simulating observations. The coefficients of determinant between simulated and observed turbidity values were 0.93 and 0.95 for Daecheong and Imha reservoirs, respectively. The outflow water quality was significantly influenced by water intake level under fully stratified condition, while the effect of intake amount was minor. In conclusion, the SELECT is simple but effective tool for supporting downstream water quality prediction and management for both reservoirs.

• 농업과학연구
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• 제49권2호
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• 한국수자원학회논문집
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• 제55권spc1호
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• pp.1295-1303
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• 2022

안정적인 수도 공급을 위한 상수도관망의 역할이 더욱 주목받음에 따라 비정상 상황에 대한 신속한 탐지와 적절한 대처 역시 중요시되고 있다. 장치에 의존한 탐지기법 등 기존의 방법론에는 한계가 존재하므로 데이터를 이용한 모델 기반의 방법이 개발되었다. 하지만 상수도관망 내 측정 데이터는 불확실성을 가져 실제 사용량과 다르다. 따라서 본 연구에서는 기계학습 방법의 하나인 인공신경망 모델을 이용하여 상수도관망 압력값을 예측함에 있어 데이터 불확실성의 영향을 조사한다. 정규분포를 따르는 임의의 값을 고려하여 데이터에 측정치 오류를 형성하고 측정치 오류 여부 및 종류에 따라 총 9가지 데이터를 인공신경망 모델을 통해 예측해 경향성을 비교한다. 분석을 통해 데이터 불확실성이 증가할수록 모델 성능이 감소하며, 출력데이터의 측정치 오류가 모델 성능에 미치는 정도가 더 큼을 확인하였다. 특히 입력데이터와 출력데이터의 측정 오차 크기가 동일한 경우 예측 정확도는 각각 72.25%, 38.61%로 큰 차이를 보였다. 따라서 ANN 모델 예측 성능 향상을 위해서는 입력 데이터보다 출력데이터인 주절점의 측정 오류 크기를 줄이는 것이 중요하다.

• 한국지반공학회논문집
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• 제15권5호
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• pp.111-124
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• 1999

중요구조물에 인접하여 지하굴착을 수행할 경우에 지반변형을 정확히 예측하여 피해를 최소화하는 것이 중요하다. 본 논문에서는 대규모의 지하수조에 인접하여 연약점성토 내에서 굴착이 수행될 때, 지반거동을 예측하기 위하여 지반조사와 실내토질실험과 함께 유한요소해석이 실시되었다. 이러한 예측과 현장계측을 통하여 흙막이벽체와 인접구조물의 거동 및 안정성이 검토되었다 지반변형에 대한 계측 및 예측결과의 비교에서 굴착공정 및 지하수위 강하를 해석시에 고려하는 것이 중요하다는 것을 보여주었다. 향후 더 좋은 예측을 위해서는 해석방법의 개선이 요구되었다.

• 대한기계학회논문집
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• 제16권4호
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• pp.753-764
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• 1992

본 연구에서는 저에가 곤란한 저출력시의 증기발생기 수위에 의한 강제정지를 감소시키기 위한 방법을 강구하기 위해 증기발생기에 대한 기존의 열수력학적 모델들 을 수정, 보완하여 증기발생기에 실제적으로 작용하는 모든 입력인자와 출력인자인 수 위와의 관계를 전달함수의 형태로 파학하였다. 즉, 수축/팽창 현상과 직접적인 관계 가 있는 강수관(downcomer)에서의 유체력(driving force)을 정확히 계산하기 위해 모 멘텀 쎌(momentum cell)이 수위에 따라 달라지게 하였으며 1차측으로 부터의 열전달을 자세히 계산하기 위해 비등시작점을 경계로 하여 단상(single-phase)영역과 이상(two -phase)영역에 대해 각기 다른 관계식을 사용하였다.

• 한국방재안전학회논문집
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• 제14권4호
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• pp.17-27
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• 2021

유량예측은 효과적인 홍수관리 및 수자원 계획을 위한 매우 중요한 재난방지 접근법이다. 현재 기후변화로 인한 집중호우가 나날이 증가하고 있어 막대한 기반시설 손실과 재산, 인명 피해가 발생하고 있다. 본 연구는 미국 테네시주 Hickman County의 Vernon에 있는 Piney Resort의 최근 홍수사례분석을 통해 최대 강우 시나리오에서 유량예측에 대한 강우의 기여도를 측정했다. Piney River 유역내 USGS 두개의 관측소(03602500, 03599500)에서 20년(2000-2019) 동안의 일별 하천 유량, 수위 및 강우 데이터를 수집했고, Long Short Term Memory(LSTM)을 사용하였다. 또한, Tensorflow, Keras Machine learning frameworks, Python을 이용하여 14일로 구별된 유량 값을 예측하였다. 또한, 모델이 2021년 8월 21일의 범람 이벤트를 예측할 수 있었는지를 결정하는 데 사용되었다. 전체 데이터(수위, 유량 및 강우량)가 포함된 LSTM 모델은 일부 강우 모델을 제외하고 지속성 모델보다 우수한 성능을 보였으며, 강우자료만 이용하여 유량예측을 하는 것은 충분하지 않음을 나타냈다. 결과는 LSTM 모델은 0.68 및 13.84m³/s의 최적 NSE 및 RMSE 값을 나타냈고, 가장 낮은 예측 오차로 예측 최대유량은 94m³/s로 나타났다. 향후 강우 패턴에 대한 다양한 분석이 이루어진다면 효율적인 홍수 경보 시스템 및 정책을 설계하는 관련 연구에 도움을 줄 것으로 판단된다.

• 한국환경과학회지
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• 제26권9호
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• pp.1031-1043
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• 2017

Recently severe drought caused the water shortage around the western parts of Chungcheongnamdo province, South Korea. A Diversion tunnel from the Geum river to the Boryong dam, which is the water supply dam for these areas has been proposed to solve this problem. This study examined hydraulic impacts on the Geum river associated with the diversion plan assuming the severe drought condition of 2015 would persist for the simulation period of 2016. The hydraulic simulation model was verified using hydrologic and hydraulic data including hourly discharges of the Geum river and its 8 tributaries, fluctuation of tidal level at the mouth of the river, withdrawals and return flows and operation records of the Geum river barrage since Feb. 1, 2015 through May 31, 2015. For the upstream boundary condition of the Geum river predicted inflow series using the nonlinear regression equation for 2015 discharge data was used. In order to estimate the effects of uncertainty in inflow prediction to the results total four inflow series consisting of upper limit flow, expected flow, lower limit flow and instream flow were used to examine hydraulic impacts of the diversion plan. The simulation showed that in cases of upper limit and expected flows there would be no problem in taking water from the Geum river mouth with a minimum water surface level of EL(+) 1.44 m. Meanwhile, the simulation also showed that in cases of lower limit flow and instream flow there would be some problems not only in taking water for water supply from the mouth of the Geum river but also operating the diversion facility itself with minimum water surface levels of EL(+) 0.94, 0.72, 0.43, and 0.14 m for the lower limit flow without/with diversion and the instream flow without/with diversion, respectively.