The water-sediment regulation scheme (WSRS) imposed on dams throughout the Yellow River not only alleviates siltation in the downstream section but also alters the nutrient characteristics, which indirectly affects the enrichment of nutrients in the estuary. Nevertheless, the long-term changes in the nutrient contents and their causes in the lower Yellow River (LYR) remain unclear, and the nutrients characteristics during the years with and without WSRS have yet to be compared. Therefore, the purpose of this study was to explore the variations in the nutrient contents and limitations at the Lijin station on the LYR over the past decade, especially during the annual WSRS period, and to compare the water quality characteristics at Lijin between the years with and without WSRS. The results reveal that WSRS significantly changed the seasonal nutrient concentrations (nitrogen, phosphorus and silicon) at the Lijin station. The fluxes of these nutrients during WSRS (excluding 2016 and 2017) accounted for 11.64–40.63% of the total annual fluxes. The N concentration in the LYR was higher than that in some global rivers, while the concentrations of dissolved inorganic phosphorus (DIP) and dissolved silica (DSi) were lower than the average levels in other rivers. In addition, higher values of dissolved inorganic nitrogen (DIN), DSi and the Redfield ratio indicated that the growth of phytoplankton at the Lijin station was strongly restricted by P. However, during the 2 years without WSRS (2016 and 2017), the proportions of the nutrient fluxes in June were less than 66% of those in the WSRS period in other years. Additionally, there was a potential Si limitation in June in these 2 years. Furthermore, due to the occurrence of floods upstream of the Yellow River and the low-level operation of the Xiaolangdi Reservoir, the fluxes of nutrients during WSRS in 2018 were approximately 0.90–4.20 times those during the same period in 2009–2015 and 6.30–35.76 times those in June 2016 and June 2017. This study shows that WSRS effectively changes the nutrient balance in the LYR and provides a reference for the multi-objective collaborative optimization of WSRS to improve siltation and control flood in the LYR.
The stability number of a breakwater can determine the armor unit’s weight, which is an important parameter in the breakwater design process. In this paper, a novel and simple machine learning approach is proposed to evaluate the stability of rubble-mound breakwaters by using Extreme Learning Machine (ELM) models. The data-driven stability assessment models were built based on a small size of training samples with a simple establishment procedure. By comparing them with other approaches, the simulation results showed that the proposed models had good assessment performances. The least user intervention and the good generalization ability could be seen as the advantages of using the stability assessment models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.