Application Of HGS Algorithm In River Flood Evolution And Reservoir Optimal Operation

Flood disasters occur frequently in our country,which seriously threatens human production and life.To some extent,it influences the economic development and the long term peace and stability of the country.Research on river flood evolution and reservoir dispatching can effectively improve the efficiency of flood control projects,reduce flood losses,and provide a foundation and technical support for the decisionmaking of flood prevention and emergency management.The solution process of traditional optimization method is complicated and highly dependent on the experience of researchers.It is difficult to obtain a better flood control dispatching scheme when solving a model with complex structure and more constraints such as reservoir dispatching.With the rapid development of artificial intelligence,the research methods of river flood evolution and reservoir optimization are more diversified and intelligent.Therefore,starting from the Hunger Game Search Algorithm(HGS),in this paper,the Dawen River Basin is chosen as the research object to execute the research on river flood evolution and reservoir optimization operation,aiming to open new ideas for solving flood model and provide reference for improving model calculation accuracy.The main research contents and achievements are as follows:(1)In order to increase the efficiency and precision of the algorithm in solving problems,the definition of parameters of HGS algorithm is studied.The orthogonal experimental design method was used to develop the experimental scheme.Under the same test conditions,the optimal value error of 13 benchmark test functions was calculated,and the optimal parameter combination was selected according to the score of Friedman test method.At the same time,the comprehensive performance of the optimal parameter combination HGS algorithm is further verified by an optimization example of groundwater inversion..Taking two groups of pumping tests as an example,four control models are designed from the perspective of intelligent optimization algorithm.Through the analysis of various error evaluation indexes,it is concluded that the solution accuracy of HGS algorithm is obviously better than that of other control models,and it performs well in parameter optimization problem.It lays a foundation for the subsequent practical application of the algorithm.Finally,the sensitivity of the inversion parameters is tested,and the results provide a reference for improving the calculation accuracy of the groundwater model.(2)In order to address the problem of parameter optimization of traditional river course evolution model and better reveal the nonlinear characteristics of river course flood evolution,in this paper,the HGS algorithm is used to calibrate the parameters of the Continuous Variable Exponential Parameter Nonlinear Muskingum model(CVEPNMM).The model was applied to Wilson,Brutsaert and Dawenkou-Daicunba river basins,and a control model is designed from two perspectives of intelligent optimization algorithm and basic model to compare the performance.The results show that the HGS algorithm has more advanced optimization performance in the optimization of parameters of CVEP-NMM model compared with other control algorithms through the flood peak error,peak time difference and flood process evaluation index,and it verifies that the HGS-CVEP-NMM model can better reflect the nonlinear evolution characteristics of river flood.(3)The ε-HGS algorithm is constructed based on the ε-HGS algorithm to solve the flood control dispatching problem of reservoir with a large number of complex constraints.This method is applied to the flood control operation of Hulushan Reservoir and Daye reservoir.Based on the maximum peak cutting criterion,the optimal flood control operation of the two reservoirs is calculated by using the respective design flood and check flood,meanwhile,the ε-DE algorithm is constructed for comparison.The results show that the average peak clipping rate of the ε-HGS algorithm is 45.5% on the designed flood and 51.6% on the checked flood,the ε-HGS algorithm is better than the ε-DE algorithm in three of the four flood dispatching events.ε-HGS algorithm has better applicability for flood scheduling problems of larger magnitude.