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Appl. Sci. 2016, 6(5), 148; doi:10.3390/app6050148

Simulation of Reservoir Sediment Flushing of the Three Gorges Reservoir Using an Artificial Neural Network

1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2
College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China
3
Institute for Aero-Engine, School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
4
Nanjing Hydraulic Research Institute, Nanjing 210029, China
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Dawson
Received: 31 March 2016 / Accepted: 4 May 2016 / Published: 18 May 2016
(This article belongs to the Special Issue Applied Artificial Neural Network)
View Full-Text   |   Download PDF [1455 KB, uploaded 18 May 2016]   |  

Abstract

Reservoir sedimentation and its effect on the environment are the most serious world-wide problems in water resources development and utilization today. As one of the largest water conservancy projects, the Three Gorges Reservoir (TGR) has been controversial since its demonstration period, and sedimentation is the major concern. Due to the complex physical mechanisms of water and sediment transport, this study adopts the Error Back Propagation Training Artificial Neural Network (BP-ANN) to analyze the relationship between the sediment flushing efficiency of the TGR and its influencing factors. The factors are determined by the analysis on 1D unsteady flow and sediment mathematical model, mainly including reservoir inflow, incoming sediment concentration, reservoir water level, and reservoir release. Considering the distinguishing features of reservoir sediment delivery in different seasons, the monthly average data from 2003, when the TGR was put into operation, to 2011 are used to train, validate, and test the BP-ANN model. The results indicate that, although the sample space is quite limited, the whole sediment delivery process can be schematized by the established BP-ANN model, which can be used to help sediment flushing and thus decrease the reservoir sedimentation. View Full-Text
Keywords: reservoir sedimentation; artificial neural network (ANN); back propagation (BP); the Three Gorges Reservoir (TGR); sediment flushing reservoir sedimentation; artificial neural network (ANN); back propagation (BP); the Three Gorges Reservoir (TGR); sediment flushing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, X.; Qiu, J.; Shang, Q.; Li, F. Simulation of Reservoir Sediment Flushing of the Three Gorges Reservoir Using an Artificial Neural Network. Appl. Sci. 2016, 6, 148.

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