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.
Highlights
Construction of the Method of an Evaluation Index System e establishment of an evaluation index system based on graph distance classification and principal components analysis can be divided into two stages: index classification and index screening, as shown in Figure 1. e first stage is the selection of the initial indexes, and the indexes are classified and selected by graph distance classification and principal components analysis
In the graph distance classification method, the highly correlated elements are ascribed to the same class. en, the representative elements that are selected from the classification are analyzed by PCA, which can reduce the information processing workload
According to the screening results of the PCA, the selected indexes and the same class indexes serve as important indexes when constructing the index system, which can avoid the loss of important indexes
Summary
Determination of the simplified index system Figure 1: Flow chart for the construction of the index system. (ii) Calculate the eigenvalues and eigenvectors of the correlation matrix R, variance contribution rate, cumulative contribution rate, and factor load of the principal components. E larger the absolute value of the factor load is, the more signi cant the in uence of the index on the evaluation results is. (iv) Screen the index according to the absolute value of the factor load of the principal component. Graph distance classi cation was used to classify the indexes, and the principal component analysis was used to select the indexes. Index Classi cation. is paper takes the seepage indexes of decommissioning uranium tailing as the research object, and the water monitoring data of six monoliths (A–F) of a decommissioning uranium tailing is targeted as the sample data. e original data originate from the environmental monitoring report of a decommissioning
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