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http://dx.doi.org/10.3741/JKWRA.2017.50.2.111

Determination of management water level for the storage and flood controls in the underflow type of multi-stage movable weir using artificial neural network  

Lee, Ji Haeng (Samhwa Environment and Construction Co. Ltd.)
Han, Il Yeong (Samhwa Environment and Construction Co. Ltd.)
Choi, Heung Sik (Department of Civil Engineering, Sangji University)
Publication Information
Journal of Korea Water Resources Association / v.50, no.2, 2017 , pp. 111-119 More about this Journal
Abstract
The underflow type movable weirs were arranged in a multi-stage way along a reach at the Chiseong River, where flooding has been observed frequently. With management water level of the movable weirs the control effects of storage and flood were suggested and the control effects were compared with those of existed weir system. The water level for the targeted storage and flood elevation was suggested by building the artificial neural network model. When the underflow type of movable weirs were arranged in a multi-stage way, the peak flood elevation decreased by 68.28% in the downstream compared with the existed weir system, and the total storage of the target section of multi-stage movable weirs increased by 216%. As a result of numerical simulation to build the artificial neural network model, 60%, 20%, and 20% among 216 data were used for the training, validation, and test, respectively. The training result of mean square error was $0.1681m^2$ and the high coefficients of determination were 0.9961, 0.9967, and 0.9943 in the training, validation, and test, respectively. As a result the water level management of each movable weir for the controls of flood elevation in the targeted downstream and targeted storage was suggested by using the artificial neural network.
Keywords
Artificial neural network; Flood control; Multi-stage; Underflow type of movable weir; Management water level;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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