Research on standardized management factors of reservoirs in Guizhou Province based on PCA-GA algorithm

Abstract

In the creation of benchmark tables relying on experts, there exists freely controlling the degree of deduction within the deduction interval. In response to the problem of inconsistent differentiation caused by the importance of factors, the proportion of total scores, and the consistency of deduction intervals, principal component analysis(PCA) is used to reconstruct the “Evaluation Criteria” and establish a feature matrix as input variables. GA genetic algorithm is used to improve the BP neural network for fitting verification. The result indicate that the higher the degree of dispersion of the factors in the Evaluation Criteria, the more representative they are of the actual situation of creation; The GA algorithm further enhances the generalization ability and accuracy of the BP neural network by establishing a mapping relationship between the explanatory target and the explanatory fitness kernel function. The improved GA-BP neural network algorithm model has a fitting accuracy of 98.78%; The fitted R² of the “Evaluation Criteria” after reconstruction is as high as 0.953, which has improved by 0.107 compared to before reconstruction and has better fitting accuracy. At present, there is relatively little research on the deconstruction and reorganization of water conservancy engineering standardization from the perspective of factor dispersion. The research conclusion of this article can effectively assist in the creation of standardized management for reservoirs in Guizhou Province and the subsequent revision of the “Evaluation Criteria”.

Keywords

reservoir standardization / principal component analysis / dispersion / genetic algorithm / refactoring standards