The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction.

Non-point source pollution inflow is one of the main causes of water quality decline in urban river networks. In this paper, aiming at the problem of non-point source pollutant transport in river network, the lateral outflow term in the Saint-Venant equation is improved from the previous constant to the time-varying flow process, and a mathematical model considering the time-varying source and sink term is established. Based on the initial rainfall intensity, surface confluence and non-point source pollutant concentration, a method for calculating the time-varying lateral pollutant input of nodes and tributaries with linear increase and exponential decay in the initial rainfall period is proposed. Based on the principle of proximity, the watershed is divided into districts. According to the principle of elevation, the non-point source pollutants are allocated to the calculation nodes of adjacent rivers in a certain proportion and incorporated into the model calculation so as to improve the mathematical model of river network water quality and apply it to the simulation of river network water quality in Maozhou River Basin. Verified by the measured data, the NSE values of the improved model are 0.805 and 0.851, respectively, indicating that the model has reliable hydrodynamic and water quality simulation accuracy, indicating that the model can be applied to the calculation of non-point source pollutants in the basin. Based on the improved model, the variation of COD concentration in the Maozhou River of Shenzhen before and after optimized water replenishment was calculated, and the time variation and spatial distribution law of the sudden drop of water quality in the river network caused by the inflow of non-point source pollution in the initial rainfall runoff and the rapid recovery after optimized water replenishment were revealed.

In response to the deficiencies in existing bridge pier scour protection technologies, this paper introduces a novel protective device, namely a normal distribution-shaped surface (BND) protection devices formed by rotating a concave normal curve. A three-dimensional turbulent SST $k-\omega$ model is constructed, and physical model experiments of conical surfaces are conducted to validate the mathematical model. The simulation analyzes longitudinal water flow, downflow, vorticity intensity, and shear stress within normal and conical surfaces. The results show that the downflow distribution in front of the pier spans a relative water depth of (−0.45, 0.67), with a peak velocity approximately 70% of the longitudinal flow velocity. Circulation forms within the surfaces, with the main vortex flux inside the BND being 33% lower than that inside the conical surface. The maximum shear stress coefficient inside the BND can reach 9, and the protective surface isolates the bed from the flow to prevent scouring by high shear stress. The velocity gradient at the edge of the surface is small, and the edge shear stress of the 3D normal distribution-shaped surface (BND) protection device is only one-third of that of the conical surface, preventing edge scouring. The large shear stress and its distribution area decrease monotonically with the increase in surface width. When the surface width is four times the diameter, the distribution range of the shear stress coefficient greater than 1 is very small. The study of three-Dimensional turbulence within the BND provides a numerical basis for an anti-scour design.