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.

With global climate warming, the frequency and intensity of typhoons are increasing, highlighting the significance of studying storm surges for coastal engineering disaster mitigation. In this study, we assessed the predictive capabilities of the new ERA5 reanalysis model and the traditional Fujita−Takahashi model for storm surges. We found that the traditional Fujita−Takahashi model, utilizing a prelandfall typhoon wind field, exhibited higher accuracy in storm surge predictions, while the ERA5 reanalysis model, employing a postlandfall wind field, demonstrated superior performance. By considering the strengths and weaknesses of both wind field models and analyzing the impact of Typhoon In-fa (2021) on the East China Sea, we determined the influence of this typhoon on storm surge heights along the eastern coastal region. These research findings provide valuable insights for the development of effective protection strategies, offering valuable references for coastal resilience planning.

In order to study the impact of artificial reef construction on marine carbon sinks, a one-year monitoring and analysis were carried out in the Sanheng artificial reef areas of Shengsi Island. The main parameters observed were sea surface temperature, salinity, pH, total alkalinity (TA), and dissolved inorganic carbon (DIC). The partial pressure of CO₂ (pCO_2w) in the surface water and the CO₂ flux (FCO₂) across the sea–air interface were calculated and analyzed. The results showed that the annual range of surface water pCO_2w was 34.48~501.53 μatm, and the partial pressure of CO₂ decreases in the following order: winter, spring, autumn, summer. The FCO₂ was significantly negatively correlated with temperature and significantly positively correlated with salinity and TA. The FCO₂ in the four seasons decreases in the following order: winter, spring, autumn, summer. The annual sea–air flux in the survey area ranged from −108.79 to 41.74 mmol m⁻² d⁻¹. In winter, both the reef area and the control area displayed positive FCO₂ values, indicating a source of CO₂, while in the other three seasons, CO₂ flux values were negative, indicating an overall CO₂ sink. Additionally, in autumn, the reef area exhibited a stronger CO₂ sink than the control area. The results indicated that the construction of artificial reefs has a certain carbon sink effect, with no significant difference in effectiveness between different artificial reef construction models.