Complementary Modeling Approach for Estimating Sedimentation and Hydraulic Flushing Parameters Using Artificial Neural Networks and RESCON2 Model

Abstract

Accurate prediction of reservoir sediment inflows (M-in) and adaptation of feasible sediment management strategies pose challenges in water engineering. This study proposed a two-stage complementary modeling approach for comprehensive reservoir sediment management. In the first stage, artificial neural network-based models provide real-time M-in predictions using water inflow, water head, and outflow as input parameters. In the second stage, the parameter estimation method of the RESCON model is applied to hydraulic flushing in a reservoir. This approach was applied to the Sangju Weir and Nakdong River Estuary Barrage (NREB) in South Korea. Results from the RESCON model revealed that hydraulic flushing was effective for sediment management at both the Sangju Weir reservoir and the NREB approach channel. Efficient flushing at the Sangju Weir required a flushing discharge of 100 m(3)/s for 6 days and 40 m of water head. Efficient flushing at the NREB required a flushing discharge of 25 m(3)/s for 6 days with 1.8 m of water-level drawdown. The proposed approach is expected to prove useful in reservoir sediment management.