심층 신경망 기법을 통한 부유사 이동 모델링
Modeling of Suspended Sediment Transport Using Deep Neural Networks
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
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.
Abstract
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.
참고문헌 (27)
- Faruk Bhuiyan, "An experimental study of mounds formed by dumping coarse sediment in channel flow", Journal of Hydraulic Research, 48(3), pp.283~291 (2010), Informa UK Limited[DOI]
- Field study of the mechanics of the placement of dredged material at open-water sites, "Bokuniewicz, H. J", US Army Engineer Waterways Experiment Station, ~, pp.Rep. No. D-78-7 (1978)
- S.G. Bolam, "Ecological consequences of dredged material disposal in the marine environment: A holistic assessment of activities around the England and Wales coastline", Marine Pollution Bulletin, 52(4), pp.415~426 (2006), Elsevier BV[DOI]
- 봉태호, "유속 및 입경에 따른 조립토의 이동 및 퇴적 특성", 한국농공학회논문집, 58(1), pp.11~17 (2016), 한국농공학회
- Buhler, J, "Environmental hydraulics", pp.193~198 (1999)
- JOHN W. M. BUSH, "Particle clouds in homogeneous and stratified environments", Journal of Fluid Mechanics, 489, pp.29~54 (2003), Cambridge University Press (CUP)[DOI]
- A. J. Cuthbertson, "Experimental Study of Fine Sand Particle Settling in Turbulent Open Channel Flows over Rough Porous Beds", Journal of Hydraulic Engineering, 133(8), pp.905~916 (2007), American Society of Civil Engineers (ASCE)[DOI]
- T.J Fredette, "Understanding the physical and environmental consequences of dredged material disposal: history in New England and current perspectives", Marine Pollution Bulletin, 49(1-2), pp.93~102 (2004), Elsevier BV[DOI]
- R. James Gensheimer, "Dynamics of Particle Clouds in Ambient Currents with Application to Open-Water Sediment Disposal", Journal of Hydraulic Engineering, 139(2), pp.114~123 (2013), American Society of Civil Engineers (ASCE)[DOI]
- Ronald J. Gibbs, "The Relationship Between Sphere Size And Settling Velocity", SEPM Journal of Sedimentary Research, 41(1), pp.7~18 (1971), Society for Sedimentary Geology[DOI]
- Glorot, X, "Understanding the difficulty of training deep feedforward neural networks", Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp.249~256 (2010)
- Jie Gu, "Experimental Study on Instantaneous Discharge of Unsorted Particle Cloud in Cross-Flow", Journal of Hydrodynamics, 20(1), pp.10~16 (2008), Springer Nature[DOI]
- Geoffrey E. Hinton, "A Fast Learning Algorithm for Deep Belief Nets", Neural Computation, 18(7), pp.1527~1554 (2006), MIT Press - Journals[DOI]
- Hochreiter S, "Field Guide to Dynamical Recurrent Neural Networks" (2001), IEEE Press
- Evaluation and calibration of the Tetra Tech Dredged material disposal models based on field data, "Johnson, B. H", US Army Engineer Waterways Experiment Station, ~, pp.Rep. No. D-78-47 (1978)
- Johnson, B. H, "Modeling dredged material disposed in open water", Proceedings of the Hydraulic Engineering sessions at Water Forum, 92, pp.1036~1041 (1992)
- Mathematical model for barged ocean disposal of wastes, "Koh, R. C. Y", US Environmental Protection Agency, ~ (1973)
- Li, R. M, "Solid particle settlement in open-channel flow", Journal of the Hydraulics Division, 101(7), pp.917~931 (1975)
- Nair, V, "Rectified linear units improve restricted boltzmann machines", Proceedings of the 27th international conference on machine learning, pp.807~814 (2010)
- Yign Noh, "Sedimentation of a particle cloud across a density interface", Fluid Dynamics Research, 27(3), pp.129~142 (2000), IOP Publishing[DOI]
- Tomonari Okada, "Estimating the spatial distribution of dredged material disposed of at sea using particle-size distributions and metal concentrations", Marine Pollution Bulletin, 58(8), pp.1164~1177 (2009), Elsevier BV[DOI]
- Rahimipour, H, "Dynamic behaviour of particle clouds", 11th Australasian Fluid Mechanics Conference, pp.743~746 (1992)
- F. Rosenblatt, "The perceptron: A probabilistic model for information storage and organization in the brain.", Psychological Review, 65(6), pp.386~408 (1958), American Psychological Association (APA)[DOI]
- Alcatraz Disposal site investigation, "Trawle, M. J", US Army Engineer Waterways Experiment Station, ~ (1986)
- Evaluating environmental effects of dredged material management alternatives - A Technical Framework, "USACE (United States Army Corps of Engineers)", ~ (2004)
- Werbos, P., "Beyond regression: New tools for prediction and analysis in the behavioral sciences", Ph.D., pp.1974
- B. Zhao, "Formation of particle clouds", Journal of Fluid Mechanics, 746, pp.193~213 (2014), Cambridge University Press (CUP)[DOI]