Constructing a Method for an Evaluation Index System Based on Graph Distance Classification and Principal Component Analysis

Abstract

Based on the importance of having an evaluation index system, a new method that combines PCA with graph distance classification is presented to make up the deficiencies of principal component analysis in the process of index screening, and this method is applied in the construction of an evaluation index system for the environmental quality of decommissioning uranium tailing. The seepage indexes were classified into six classes using graph distance classification, which selects the representative elements, including pH, ∑α, 210Pb, 210Po, F−, and NO3−. All of the representative elements were analyzed by PCA while determining the seepage indexes, including pH, U, Ra, ∑α, NH4-N, and F−, and establishing an index system for environmental quality evaluation that consists of two primary indexes (seepage and radiation environment) and 12 secondary indexes. The results showed that the model had ensured that the sifted indexes had a significant effect on the evaluation result and avoided the deletion of some important indexes and that it had stronger applicability and maneuverability.

Evaluation and future framework of green mine construction in China based on the DPSIR model

“…It has been widely recognized that PCA can reduce the information processing workload and avoid repeated information analysis. Therefore, PCA has strong applicability in index selection, evaluation, and prediction [34].…”

“…The next step after dimensionality reduction is to calculate the principal component loading and then carry out the quantitative evaluation. The main steps of PCA, as used in the literature of evaluation index [34,35], are: 1) In the first step, data were standardized to improve the accuracy of data analysis and eliminate the influence of the data dimension. Eq.…”