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Volume 2018, Article ID 9205025, 10 pages
Research Article

Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

1School of Resources and Environment Engineering, Henan Polytechnic University, Henan, Jiaozuo 454000, China
2Collaborative Innovation Center of Coalbed Methane and Shale Gas for Central Plains Economic Region, Henan, Jiaozuo 454000, China

Correspondence should be addressed to Pinghua Huang; nc.ude.uph@1002hph

Received 12 September 2017; Revised 14 January 2018; Accepted 30 January 2018; Published 26 February 2018

Academic Editor: Cinzia Federico

Copyright © 2018 Pinghua Huang and Xinyi Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Source discrimination of mine water plays an important role in guiding mine water prevention in mine water management. To accurately determine water inrush source from a mine in the Jiaozuo mining area, a Piper trilinear diagram based on hydrochemical experimental data of stratified underground water in the area was utilized to determine typical water samples. Additionally, principal component analysis (PCA) was used for dimensionality reduction of conventional hydrochemical variables, after which mutually independent variables were extracted. The Piper-PCA-Fisher water inrush source recognition model was established by combining the Piper trilinear diagram and Fisher discrimination theory. Screened typical samples were used to conduct back-discriminate verification of the model. Results showed that 28 typical water samples in different aquifers were determined through the Piper trilinear diagram as a water sample set for training. Before PCA was carried out, the first five factors covered 98.92% of the information quantity of the original data and could effectively represent the data information of the original samples. During the one-by-one rediscrimination process of 28 groups of training samples using the Piper-PCA-Fisher water inrush source model, 100% correct discrimination rate was achieved. During the prediction and discrimination process of 13 samples, one water sample was misdiscriminated; hence, the correct prediscrimination rate was 92.3%. Compared with the traditional Fisher water source recognition model, the Piper-PCA-Fisher water source recognition model established in this study had higher accuracy in both rediscrimination and prediscrimination processes. Thus it had a strong ability to discriminate water inrush sources.