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The Xiaolangdi Dam is a key project for the control and development of the Yellow River. It bears the functions of flood control, controlling water and sediment in the lower reaches, ice prevention, industrial and agricultural water supply, power generation, and so on. Its safety is related to people's life and property safety and local economic and social development. It is of great significance to carry out comprehensive and regular deformation monitoring for dams since the deformation is an important evaluation index for dam safety. Interferometric Synthetic Aperture Radar (InSAR) technology has been a rapidly evolving technology in the field of space geodesy in recent years. It offers advantages such as high monitoring precision, extensive coverage, and high monitoring point density, making it a powerful tool for monitoring deformations in hydraulic engineering projects. Based on Sentinel-1 data covering the Xiaolangdi Dam from September 2020 to November 2022, the PS-InSAR technique was used to obtain the surface deformation of the Xiaolangdi Dam, and reservoir water level data on image acquisition dates were obtained for joint analysis. The results show that there is a large deformation in the center of the dam crest of the Xiaolangdi Dam, while both sides of the slope and downstream dam foot are relatively stable. The time series deformation of the dam body is closely related to the reservoir water level change. When the water level increases, the dam body tends to deform downstream; when the water level decreases, the dam body tends to deform upstream. The deformation and water level of the Xiaolangdi Dam exhibit a clear negative correlation. There is no significant cumulative deformation on the dam slopes or at the base of the dam. However, cumulative deformation occurs over time in the central area of the dam's crest. The deformation process at the central area of the dam's crest follows a continuous and non-disruptive pattern, which is consistent with the typical deformation behavior of the Xiaolangdi earth–rock dam structure. Therefore, it is judged that the current deformation of the Xiaolangdi Dam does not impact the safe operation of the dam. InSAR technology enables the rapid acquisition of high-precision, high-density deformation information on the surfaces of reservoir dams. With an increasing number of radar satellites in various frequency bands, such as Sentinel-1 and TerraSAR-X, there is now an ample supply of available data sources for InSAR applications. Consequently, InSAR technology can be extended to routine monitoring applications for reservoir dam deformations, especially for small and medium-sized reservoirs that may not be equipped with ground measurement tools like GNSS. This holds significant importance and potential for enhancing the safety monitoring of such reservoirs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]