한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제60권4호
  • /
  • Pages.83-91
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  • 2018
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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심층 신경망 기법을 통한 부유사 이동 모델링

Modeling of Suspended Sediment Transport Using Deep Neural Networks

  • Bong, Tae-Ho (Oregon State University) ;
  • Son, Young-Hwan (Department of Rural Systems Engineering and Research Institute for Agriculture and Life Sciences, Seoul National University) ;
  • Kim, Kyu-Sun (Samsung C&T Engineering & Construction Group) ;
  • Kim, Dong-Geun (Department of Rural Systems Engineering, Seoul National University)
  • 투고 : 2018.04.12
  • 심사 : 2018.06.12
  • 발행 : 2018.07.31
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초록

Land reclamation, coastal construction, coastline extension and port construction, all of which involve dredging, are increasingly required to meet the growing economic and societal demands in the coastal zone. During the land reclamation, a portion of landfills are lost from the desired location due to a variety of causes, and therefore prediction of sediment transport is very important for economical and efficient land reclamation management. In this study, laboratory disposal tests were performed using an open channel, and suspended sediment transport was analyzed according to flow velocity and grain size. The relationships between the average and standard deviation of the deposition distance and the flow velocity were almost linear, and the relationships between the average and standard deviation of deposition distance and the grain size were found to have high non-linearity in the form of power law. The deposition distribution of sediments was demonstrated to have log-normal distributions regardless of the flow velocity. Based on the experimental results, modeling of suspended sediment transport was performed using deep neural network, one of deep learning techniques, and the deposition distribution was reproduced through log-normal distribution.

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참고문헌

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