The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m3, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups.
The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.
This paper, based on daily rainfall erosivity model, ArcGIS, trend analysis and Kriging interpolation method, analyzed the spatial and temporal distribution characteristics of rainfall erosivity in the Luojiang River Basin of China, and then explored the influence relationship between land use change types and rainfall erosivity potential. The results showed the following: (1) from 1980 to 2019, the distribution range of multi-annual rainfall erosivity in the Luojiang River Basin was 14,674–15,227 MJ·mm/ (hm2·h), with an average value of 14,102 MJ·mm/(hm2·h), showing an overall increasing trend; (2) the spatial distribution of rainfall erosivity value tends to be consistent with the multi-year average rainfall, showing a decreasing trend from the middle to the periphery of the basin; (3) land use change is an important factor affecting the spatial and temporal distribution characteristic of rainfall erosivity value in the basin. The increase in rainfall erosivity will undoubtedly increase the potential of soil erosion. This study can provide theoretical reference for future basin land use planning and put forward preventive suggestions according to the distribution characteristics of rainfall erosivity.
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