Multi-objective genetic algorithms (MOGAs) are widely used for multi-reservoir systems’ optimization due to their high efficiency and fast convergence. However, the computational cost grows exponentially with the expansion of multi-reservoir systems and the increased dimensions of optimization problems, posing a great challenge to multi-reservoir operations. Therefore, it is important to find a suitable and efficient multi-objective algorithm for multi-reservoir system optimization. In this study, three representative MOGAs were selected, namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-Dominated Sorting Genetic Algorithm III (NSGA-III), and Reference Vector Guided Evolutionary Algorithm (RVEA), and three multi-objective optimization models were then developed accordingly. Numerical experiments were conducted to evaluate the performance of these three algorithms for the optimization of a cascaded reservoir system. The results show that for the two-objective model, the non-dominant size generation rate (NDSGR) of NSGA-II is 1.66 and 4.01 times that of NSGA-III and RVEA, respectively. NSGA-II is more suitable for solving the optimization operation problem with two objectives. Based on the comprehensive evaluation, NSGA-III seems to be more appropriate for more than two objectives. These results emphasize the importance of using appropriate algorithms and provide further insights into the choice of algorithms for the optimization of hydropower systems.

Due to the impact of climate change and rapid urbanization, issues around global urban flood control and water environment security have emerged as major global concerns. As a practical way to address these issues, exploitation of urban rainwater resources has become a worldwide hotspot for research and application. This paper 1) briefly examined the evolution of rainwater utilization management modes in advanced countries, 2) classified urban rainwater utilization measures from the utilization stages into three categories—source control, medium transmission, and terminal treatment, 3) summarized the advantages, disadvantages, and scope of the application of these measures, 4) reviewed the benefits, drawbacks, and application areas of these measures, and 5) conducted a quantitative analysis of their impact on rainwater pollution and flood control.

Submerged vegetation changes the hydrodynamic characteristics of rivers, lakes, wetlands, and coastal zones. However, only a few studies have focused on the effect of flexible submerged vegetation on hydrodynamic characteristics under unidirectional flow. Therefore, laboratory experiments were conducted to study the effects of submerged vegetation with different flexibility on the flow structure and turbulence characteristics under unidirectional flow. The results showed that the reconfiguration and coordination of wave motion of flexible submerged vegetation redistribute flow velocity, Reynolds stress, and turbulent kinetic energy inside and outside of the vegetation canopy. With a gradual decrease in the deflection height of vegetation, the differences in dimensionless velocity, dimensionless mixed layer thickness, bulk drag coefficient, averaged turbulent kinetic energy, and the averaged contribution rate of its shear production term for the vegetation canopy also decrease; the trend of the penetration depth of Reynolds stress is opposite. Based on the turbulent kinetic energy budget equation, a turbulent kinetic energy model (TKE model) was established, which can be used to predict the turbulent kinetic energy and its shear production term within the vegetation canopy. Here, the scaling factor was determined by the vegetation canopy Cauchy number. The TKE model can be applied under unidirectional flow conditions for submerged vegetation with different flexibilities with high accuracy. It is a simple method to predict vegetation-induced turbulence and the characteristics of sediment and material transport under the influence of submerged vegetation with different flexibility.