Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network |
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Authors: | LI YITIAN ROY R GU |
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Institution: | (1) The Key Laboratory of Water and Sediment Sciences of Ministry of Education of China, Wuhan University, Wuhan, China, 430072, CN;(2) Department of Civil and Construction Engineering, Iowa State University, Ames, Iowa 50011, USA, US |
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Abstract: | A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes
are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important
that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting
flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations
into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological
applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling
daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the
comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction
of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model
flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant
loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles
into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance
and interpretability while at the same time making the model more understandable to the engineering community. |
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Keywords: | : Artificial neural networks River system Streamflow Sediments Water resources management |
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