한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제51권1호
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  • Pages.71-80
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  • 2018
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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인공신경망 모형을 이용한 도달시간의 신뢰성 평가 -온천천 유역을 대상으로-

Reliability evaluations of time of concentration using artificial neural network model -focusing on Oncheoncheon basin-

  • 윤의혁 (부산대학교 사회환경시스템공학과) ;
  • 박종빈 (부산대학교 녹색국토물관리연구소) ;
  • 이재혁 (부산대학교 녹색국토물관리연구소) ;
  • 신현석 (부산대학교 사회환경시스템공학과)
  • Yoon, Euihyeok (Department of Civil Engineering, Pusan National University) ;
  • Park, Jongbin (Green Land & Water Management Research Institute, Pusan National University) ;
  • Lee, Jaehyuk (Green Land & Water Management Research Institute, Pusan National University) ;
  • Shin, Hyunsuk (Department of Civil Engineering, Pusan National University)
  • 투고 : 2017.09.21
  • 심사 : 2017.11.17
  • 발행 : 2018.01.31
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초록

하천 관리에 있어 도달시간은 중요한 인자 중의 하나이다. 특히 사회적으로 다양한 하천 활용에 대한 요구가 높아짐에 따라 친수공간으로써 하천에서의 정확한 도달시간 산정은 홍수시 주민 대피 시간 확보 등을 위해서 매우 중요하다. 그러나 과거 도달시간 산정에 대한 연구는 자연 하천의 복합 유역에서의 단일 수문사상에 대하여 연구가 수행되어왔으며, 도심하천의 단일유역을 대상으로 복합 수문 사상에 대한 도달시간 산정방법의 개발은 미흡한 실정이다. 따라서 최근 집중호우에 의하여 빈번한 침수 피해가 발생된 부산광역시 대표 도심하천인 온천천 유역에 대하여 과거 10년(2006~2015년) 동안의 강우-유출량 자료를 이용하여 도달시간을 산정하였고, Matlab 기반의 인공신경망 기법을 이용하여 신뢰성을 검토하였다. 12시간 이상 무강우를 기준으로 총 254개의 강우 사상을 분리하였고, 이를 바탕으로 총 강우량, 총 유출량, 첨두 강우량/총 강우량, 첨두 유출량/총 유출량, 지체시간, 도달시간 등 총 6개의 변수를 산정하여 인공신경망 모형의 훈련 및 검증에 활용하였다. 그 결과 훈련에 과 예측 및 검증에 활용된 입력 변수의 상관관계는 각 각 0.807 및 0.728로 나타났으며, 연구결과를 바탕으로 도심하천의 도달시간 산정결과의 신뢰성 분석에 이를 활용할 수 있을 것으로 판단된다.

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

키워드

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