Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Determination of management water level for the storage and flood controls in the underflow type of multi-stage movable weir using artificial neural network

인공신경망을 이용한 다단 배치된 하단배출형 가동보의 저류 및 홍수 조절을 위한 관리수위 결정

  • Received : 2016.12.02
  • Accepted : 2017.01.16
  • Published : 2017.02.28
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Abstract

The underflow type movable weirs were arranged in a multi-stage way along a reach at the Chiseong River, where flooding has been observed frequently. With management water level of the movable weirs the control effects of storage and flood were suggested and the control effects were compared with those of existed weir system. The water level for the targeted storage and flood elevation was suggested by building the artificial neural network model. When the underflow type of movable weirs were arranged in a multi-stage way, the peak flood elevation decreased by 68.28% in the downstream compared with the existed weir system, and the total storage of the target section of multi-stage movable weirs increased by 216%. As a result of numerical simulation to build the artificial neural network model, 60%, 20%, and 20% among 216 data were used for the training, validation, and test, respectively. The training result of mean square error was $0.1681m^2$ and the high coefficients of determination were 0.9961, 0.9967, and 0.9943 in the training, validation, and test, respectively. As a result the water level management of each movable weir for the controls of flood elevation in the targeted downstream and targeted storage was suggested by using the artificial neural network.

치성천의 홍수범람이 빈번하게 발생하는 구간을 대상으로 하단배출형 가동보를 다단으로 배치하여 가동보의 관리수위별 저류 및 홍수조절 효과를 기존 고정보의 설치 경우와 비교분석하였다. 분석 결과를 기반으로 인공신경망 모형을 구축하여 목표하는 저류량과 하류부 홍수위 조절을 위한 가동보의 관리수위를 제안하였다. 하단배출형 가동보를 다단으로 배치할 경우 고정보 대비 하류부에서의 첨두 홍수위가 68.28%가 감소하였고, 대상구간의 총 저류량이 216%가 증가하였다. 인공신경망 학습모델의 구축을 위해 수치모의 결과 216개의 data 중 60%, 20%, 20%를 각각 학습, 검증 및 시험에 사용하였다. 학습결과 평균제곱오차가 $0.1681m^2$, 결정계수가 학습, 검증 및 시험에서 각각 0.9961, 0.9967, 0.9943으로 높게 나타났다. 인공신경망을 이용하여 목표하는 저류량과 하천의 하류부에서의 홍수위에 대한 각각 가동보의 관리수위의 결정방안을 제시하였다.

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References

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