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.

A one-dimensional water quality mathematical model was put forward to calculate the water environmental carrying capacity(WECC) for rivers in Huaxi District. The river system of Huaxi district is divided into four basins. Based on field observation and numerical simulation, the pollutant degradation coefficients and diffusion coefficient for different river districts were determined to explore the WECCs of COD, NH₃-N and TP. It reflects the pollutant carrying capacity of different basins under the hydrological condition of 90% guarantee rate, which plays an important role in pollution control and water environmental protection.

Submarine pipelines are the lifelines of the national economy. Under the influence of typhoons, high-speed currents and waves continuously erode the seabed, leading to suspension or even rupture of pipelines. Therefore, it is of great importance to study the sediment transport under the action of waves and currents. A numerical model of sediment scouring and deposition combining wave and currents is established, which considered tidal current, storm surges, wind waves, and sediments in the East China Sea. Combining with the monitoring of the actual laying condition of pipelines, it is found that the area with the most serious scouring is around KP300. It is shown that the typhoon weather with high intensity and density will lead to the suspension of pipelines, which is noteworthy in the construction of marine engineering.

A prediction model for the local scouring depth at bridge piers is built using machine learning. Measured data which is collected from laboratory tests and field observation is used to train the predicting model which is verified by the measured data of Hangzhou Bay Bridge. The result shows that the predicted value of the local scouring depth at piers obtained by the machine learning model is in good agreement with the measured ones, which is more suitable than existing general formula. The machine learning method is effective in predicting the local scour depth at bridge piers.