State of the Practice Worldwide: HCA‐PCA‐EWM Discrimination Model of Water Inrush Source in Mines

Abstract

AbstractThe hydrogeological conditions in the North China type coal mine area are complex and diverse, and water gushing accidents are frequent. Quickly and accurately identifying the source of water in these incidents is necessary to reduce the harmful effects of mine floods. Taking the Pingdingshan mining area as an example, what chemical composition data from each of the main aquifers was collected and a mine gushing water source model, based on Hierarchical Cluster Analysis—Principle Component Analysis—Entropy Weighted Membership (HCA‐PCA‐EWM), was established. First, typical water sample data from an aquifer were selected by systematic cluster analysis. Then, the remaining water samples were randomly divided into training and prediction samples. Principal component analysis was used to reduce the dimension of water chemical components in training samples. Finally, the discriminant model of the water gushing source was established on the basis of the entropy weight‐membership degree principle. The accuracy of the HCA‐PCA‐EWM discriminant model approached 100%, while those of traditional PCA‐Fisher and PCA‐EWM discriminant models were 62.5 and 87.5%, respectively. From the above, the HCA‐PCA‐EWM discriminant model can effectively select typical aquifer water samples, significantly improve the accuracy of the model, and provide a theoretical basis for the effective identification of mine water gushing sources.