## 1 Introduction  
Building reservoirs on rivers, especially sandy rivers, breaks the natural equilibrium state of flow and sediment conditions, as well as riverbed morphology. The lifting of the water level increases water depth, slows down the current velocity, and thus reduces the sediment carrying capacity of water, leading to a large number of sediment deposits in the reservoir. The global capacity loss of reservoirs takes up to 0.5%-1% of the total reservoir storage every year, approximately 45 km\({}^{3}\)[1]. In addition to capacity loss, which disables the design function of reservoirs such as flood control, power generation, irrigation, and water supply, reservoir sedimentation also shortens the serve life of the reservoir, enlarges the flooded and submerged area upstream, threatens the safety upstream, and impacts navigation. The reduction of outflow sediment also results in erosion of riverbeds downstream, and brings about a series of new problems. There are plenty of sandy rivers in China. According to incomplete statistics, the annual sediment runoff of 11 main rivers in China is about 16.9 \(\times\) 10\({}^{8}\) t [2]. The annual sediment runoff at the Cuntan hydrometric station of the Yangtze River is up to 2.10 \(\times\) 10\({}^{8}\) t in the year 2012 [3]. With the construction of more and more large-scale reservoirs, the sedimentation problem has become prominent. The world-renowned Three Gorges Reservoir (TGR) is large multi-objective comprehensive water control project with functions of flood control, power generation, navigation, water supply, and so on. Ever since its design and demonstration phase, the sedimentation problem has been one of the principal concerns. Particle size of both the inflow and outflow sediment of the TGR presents a decreasing tendency, especially after the construction of TGR as well as other large-scale reservoirs upstream. The median particle diameter of the inflow sediment into the TGR is around 0.01-0.011 mm, while that of the outflow sediment is about 0.007-0.009 mm [3]. More than 85% of the inflow sediment particles are within 0.062 mm, and only 3%-7% are larger than 0.125 mm. Thus, flushing is believed to be an effective way to transport sediment. In order to make better use of reservoir release to flush sediment, the relationship between the amount of flushed sediment and its influence factors needs to be studied. However, sedimentation affected by geographical location, topography, geology, climate, and other natural factors, as well as human activities, is a very complex and cross-disciplinary subject. It involves river dynamics, geology, geography and other subjects with immature development. Both the complexity and the immaturity lead to the difficulties of solving the reservoir sedimentation problem. Calculation of sediment erosion and deposition can adopt the mathematical model of flow and sediment. There have been a range of mathematical models presented in previous publications [4; 5; 6; 7; 8; 9], the theoretical foundation of which are mainly composed of: flow continuity equation, flow motion equation, sediment continuity equation, riverbed deformation equation, and sediment-carrying capacity equation. Establishing and solving a comprehensive set of equations for flow and sediment mathematical models is a complex task with requirements of extensive data to get closed conditions. Currently, many important aspects of the sediment transportation rely on experience and subjective judgment, which is not conducive to close the mathematical model. Furthermore, reservoir sedimentation cannot be generalized with a simple 1D mathematical model, while the 2D and 3D models for sediment transport are only available for short river reach or partial fluvial process issues due to their complex structure, the large number of nodes, and time-consuming computation [10; 11; 12]. The artificial neural network (ANN) model has the characteristics of parallelism, robustness, and nonlinear mapping.