• 제목/요약/키워드: Reservoir Level Prediction

검색결과 27건 처리시간 0.021초

LSTM 기반 배수지 수위 변화 예측모델과 적합성 평가 연구 (A Study on LSTM-based water level prediction model and suitability evaluation)

  • 이은지;박형욱;김은주
    • 스마트미디어저널
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    • 제11권5호
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    • pp.56-62
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    • 2022
  • 배수지는 정수처리 된 물을 급수하기 위해 정수물을 모아두는 저장소로서, 물의 수요량에 따라 급수량을 조절하여 안정적으로 물을 공급하기 위해 배수지의 수위 관리는 매우 중요하다. 현재 배수지 내에 수위 계측 센서를 설치하여, 가압장의 펌프운영을 통해 배수지의 최적 수위를 관리하고 있으나, 센서의 오작동 및 통신두절 등 사고대응을 관리자 감시에 의존하고 있어, 사고의 위험을 안고 있다. 본 연구에서는 배수시설의 안정적 운영을 위하여, 배수지의 수위 변화 예측 인공지능 모델을 제안하였으며, 배수지 수위 변화 예측모델의 현장적용에 대한 안정성을 확인하기 위하여 수위 데이터의 결측 상황에 대한 시뮬레이션을 통하여, 실제 수위 변화값과 예측된 수위 변화값의 비교를 통하여 모델의 유용성을 확인하였다.

자동기계학습 TPOT 기반 저수위 예측 정확도 향상을 위한 시계열 교차검증 기법 연구 (A Study on Time Series Cross-Validation Techniques for Enhancing the Accuracy of Reservoir Water Level Prediction Using Automated Machine Learning TPOT)

  • 배주현;박운지;이서로;박태선;박상빈;김종건;임경재
    • 한국농공학회논문집
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    • 제66권1호
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    • pp.1-13
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    • 2024
  • This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R2 (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

수질향상을 위해 예측을 이용한 환경 친화적인 저수조 관리 (ECO-Friendly Reservoir Tank Management using Prediction for Improved Water Quality)

  • 정경용;조선문
    • 한국콘텐츠학회논문지
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    • 제9권6호
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    • pp.9-16
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    • 2009
  • 수자원 관리 서비스를 위한 인프라스트럭처가 구축되면서, 환경 친화적인 저수조 관리의 중요성이 부각되고 있다. 본 논문에서는 수질 향상을 하고 저수조를 온라인 관리하기 위하여 예측을 이용한 환경 친화적인 저수조 관리를 제안하였다. 제안된 방법에서는 저수조의 상황과 환경을 정의하였고 협력적 필터링을 이용하여 펌프동작, 태양전지, 약품, 저수위, 전화회선, 모뎀에 따른 적합한 서비스를 예측하였다. 예측을 이용한 환경 친화적인 저수조 관리 시스템의 성능 평가를 하기 위해 대응표본 T-검정을 실시하여 유용성을 검증하였다. 평가 결과, 서비스에 대한 만족도의 차이가 통계적으로 의미가 있음을 증명하였고 높은 만족도를 보임을 확인하였다. 따라서 상황 정보 및 환경정보를 제공하여 효율적인 예측에 대한 만족도와서비스의 질을 향상시켰다.

다변량 입력이 딥러닝 기반 저수율 예측에 미치는 영향 분석과 중장기 예측 방안 (Analyzing the Impact of Multivariate Inputs on Deep Learning-Based Reservoir Level Prediction and Approaches for Mid to Long-Term Forecasting)

  • 박혜승;윤종욱;이호준;양현호
    • 정보처리학회 논문지
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    • 제13권4호
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    • pp.199-207
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    • 2024
  • 지역 저수지들은 농업용수 공급의 중요한 수원공으로 가뭄과 같은 극단적 기후 조건을 대비하여 안정적인 저수율 관리가 필수적이다. 저수율 예측은 국지적 강우와 같은 지역적 기후 특성뿐만 아니라 작부시기를 포함하는 계절적 요인 등에 크게 영향을 받기 때문에 적절한 예측 모델을 선정하는 것만큼 입/출력 데이터 간 상관관계 파악이 무엇보다 중요하다. 이에 본 연구에서는 1991년부터 2022년까지의 전라북도 400여 개 저수지의 광범위한 다변량 데이터를 활용하여 각 저수지의 복잡한 수문학·기후학적 환경요인을 포괄적으로 반영한 저수율 예측 모델을 학습 및 검증하고, 각 입력 특성이 저수율 예측 성능에 미치는 영향력을 분석하고자 한다. 신경망 구조에 따른 저수율 예측 성능 개선이 아닌 다변량의 입력 데이터와 예측 성능 간의 상관관계에 초점을 맞추기 위하여 실험에 사용된 예측 모델로 합성곱신경망 또는 순환신경망과 같은 복잡한 형태가 아닌 완전연결계층, 배치정규화, 드롭아웃, 활성화 함수 등의 조합으로 구성된 기본적인 순방향 신경망을 채택하였다. 추가적으로 대부분의 기존 연구에서는 하루 단위의 단기 예측 성능만을 제시하고 있으며 이러한 단기 예측 방식은 10일, 한 달 단위 등 중장기적 예측이 필요한 실무환경에 적합하지 않기 때문에, 본 연구에서는 하루 단위 예측값을 다음 입력으로 사용하는 재귀적 방식을 통해 최대 한 달 뒤 저수율 예측 성능을 측정하였다. 실험을 통해 예측 기간에 따른 성능 변화 양상을 파악하였으며, Ablation study를 바탕으로 예측 모델의 각 입력 특성이 전체 성능에 끼치는 영향을 분석하였다.

불확실성을 고려한 통합유역모델링 (Integrated Watershed Modeling Under Uncertainty)

  • 함종화;윤춘경;다니엘 라욱스
    • 한국농공학회논문집
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    • 제49권4호
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    • pp.13-22
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    • 2007
  • The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

2차원 유사운송모형을 이용한 저수지 퇴적분포유형의 추정 (Prediction of Reservoir Sedimentation Patterns Using a Two-Dimensional Transport Model)

  • 이봉훈;박창헌;박승우
    • 한국농공학회지
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    • 제35권1호
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    • pp.50-58
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    • 1993
  • The sedimentation patterns at a reservoir, important to the reservoir capacity curve were simulated using a depth averaged, two-dimensional sediment transport model, that is capable of depicting velocity distributions and sediment transportation. The Banweol reservoir, whose stage capacity relationships have been surveyed before and after the construction, was selected and the daily inflow rates and stages were simulated using a reservoir operation model(DI-ROM). The applicability of the transport model was tested from the comparisons of simulated sedimentation patterns to the surveyed results. The simulated inflow rates and water level fluctuations at the reservoir during twenty-one years from 1966 to 1986, showed that water levels exceeding 80 percent of the total capacity occurred for 70 percent of the periods and inflow rates less than 5000rn$^3$/day sustained for 54 percent of the spans. Dorminant flow directions were simulated from two streamflow inlets to the dam site. And simulated sediment concentrations were higher near the inlets and lower at the inside of the reservoir. Sediment was deposited heavily near the inlets, and portions of sediments were distributed along the flow paths within the reservoir. The comparisons between the simulation results and the surveyed depositions were partially matched. However, it was not possible to compare two results at the upper parts of the reservoir where dredging was carried out few times for the purpose of reservoir maintenance. This study demonstrates that sedimentation patterns within the reservoir are closely related to incoming sediment and flow rates, water level fluctuations, and flow circulation within the reservoir.

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A Comparative Study on Reservoir Level Prediction Performance Using a Deep Neural Network with ASOS, AWS, and Thiessen Network Data

  • Hye-Seung Park;Hyun-Ho Yang;Ho-Jun Lee; Jongwook Yoon
    • 한국컴퓨터정보학회논문지
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    • 제29권3호
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    • pp.67-74
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    • 2024
  • 본 논문에서는 기후 변화와 지속 가능한 수자원 관리의 중요성이 증가하는 가운데, 다양한 강우 측정 방법이 저수지 수위 예측 성능에 미치는 영향을 분석하기 위한 연구를 제시한다. 이를 위해 우리는 기상정보개방포털에서 제공하는 종관기상관측장비인 ASOS의 관측 강우, 자동기상관측장비인 AWS의 관측 강우, 그리고 면적강우비에 따라 재산정된 티센망 기반의 강우 데이터를 활용하여 신경망 기반 저수율 예측 모델에 대한 학습을 각각 수행하고, 학습된 모델의 예측 성능을 비교 및 분석하였다. 전라북도 소재 34개의 저수지에 대한 실험을 통해 각 강우량 측정방식이 저수율 예측 정확도 향상에 얼마나 기여하는지 조사하였다. 연구 결과, 티센망 기반의 강우 면적비를 활용한 저수지 강우 데이터가 가장 높은 예측 정확도를 제공한다는 것을 밝혀냈다. 이는 티센망이 주변 관측소들 사이의 정확한 거리를 고려함으로써 각 관측소가 대표하는 지역의 경계를 정의함으로써 각 지역의 실제 강우 상황을 더 정확하게 반영하기 때문이다. 이러한 발견은 정확한 지역 강우 데이터 학습이 저수율 예측에 있어 결정적인 요인 중 하나임을 시사한다. 더불어, 이 연구는 정밀한 강우 측정 및 데이터 분석의 중요성을 강조하며, 농업, 도시 계획, 홍수 관리와 같은 다양한 분야에서 예측 모델의 잠재적 응용 가능성을 제시한다.

Sentinel-2 위성영상을 활용한 농업용 저수지 가용수량 추정 (Estimation of Water Storage in Small Agricultural Reservoir Using Sentinel-2 Satellite Imagery)

  • 이희진;남원호;윤동현;장민원;홍은미;김태곤;김대의
    • 한국농공학회논문집
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    • 제62권6호
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    • pp.1-9
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    • 2020
  • Reservoir storage and water level information is essential for accurate drought monitoring and prediction. In particular, the agricultural drought has increased the risk of agricultural water shortages due to regional bias in reservoirs and water supply facilities, which are major water supply facilities for agricultural water. Therefore, it is important to evaluate the available water capacity of the reservoir, and it is necessary to determine the water surface area and water capacity. Remote sensing provides images of temporal water storage and level variations, and a combination of both measurement techniques can indicate a change in water volume. In areas of ungauged water volume, satellite remote sensing image acts as a powerful tool to measure changes in surface water level. The purpose of this study is to estimate of reservoir storage and level variations using satellite remote sensing image combined with hydrological statistical data and the Normalized Difference Water Index (NDWI). Water surface areas were estimated using the Sentinel-2 satellite images in Seosan, Chungcheongnam-do from 2016 to 2018. The remote sensing-based reservoir storage estimation algorithm from this study is general and transferable to applications for lakes and reservoirs. The data set can be used for improving the representation of water resources management for incorporating lakes into weather forecasting models and climate models, and hydrologic processes.

성층화된 저수지의 방류수 수질예측을 위한 SELECT 모델의 적용성 검토 (Evaluation of SELECT Model for the Quality Prediction of Water Released from Stratified Reservoir)

  • 이흥수;정세웅;신상일;최정규;김유경
    • 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.

CAT을 이용한 저수지 수위 예측 (Prediction of Reservoir Water Level using CAT)

  • 장철희;김현준;김진택
    • 한국농공학회논문집
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    • 제54권1호
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    • pp.27-38
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    • 2012
  • This study is to analyse the hydrological behavior of agricultural reservoir using CAT (Catchment hydrologic cycle Assessment Tool). The CAT is a water cycle analysis model in order to quantitatively assess the characteristics of the short/long-term changes in watershed. It supports the effective design of water cycle improvement facilities by supplementing the strengths and weaknesses of existing conceptual parameter-based lumped hydrologic models and physical parameter-based distributed hydrologic models. The CAT especially supports the analysis of runoff processes in paddy fields and reservoirs. To evaluate the impact of agricultural reservoir operation and irrigation water supply on long-term rainfall-runoff process, the CAT was applied to Idong experimental catchment, operated for research on the rural catchment characteristics and accumulated long term data by hydrological observation equipments since 2000. From the results of the main control points, Idong, Yongdeok and Misan reservoirs, the daily water levels of those points are consistent well with observed water levels, and the Nash-Sutcliffe model efficiencies were 0.32~0.89 (2001~2007) and correlation coefficients were 0.73~0.98.