2021
DOI: 10.1155/2021/6663827
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Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Abstract: Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54… Show more

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Cited by 11 publications
(6 citation statements)
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“…Fig. 5 B depicts that all the R 2 and Q2 scores to the left are less than those on the extreme right, implying a lack of overfitting in the altitude-specific dark tea classification approach ( Bi et al, 2021 ). The R 2 X, R 2 Y, and Q 2 scores recorded as 0.860, 1, and 0.915, respectively, suggest that the variation among dark teas from varied origins after the post-intermediate data fusion was significant, and the resulting approach designated strong predictive performance.…”
Section: Resultsmentioning
confidence: 99%
“…Fig. 5 B depicts that all the R 2 and Q2 scores to the left are less than those on the extreme right, implying a lack of overfitting in the altitude-specific dark tea classification approach ( Bi et al, 2021 ). The R 2 X, R 2 Y, and Q 2 scores recorded as 0.860, 1, and 0.915, respectively, suggest that the variation among dark teas from varied origins after the post-intermediate data fusion was significant, and the resulting approach designated strong predictive performance.…”
Section: Resultsmentioning
confidence: 99%
“…However, to obtain a higher level of treatment separation and a better understanding of the variables responsible for classification, a supervised PLS-DA was applied. The PLS-DA, in contrast to PCA, is a supervised approach that can categorize observations into groups based on the greatest predicted indicator variable [ 49 , 50 ]. Barker, in 2012 [ 51 ], employed statistical theory to demonstrate that PLS-DA was capable of accurate classification.…”
Section: Resultsmentioning
confidence: 99%
“…Principal component analysis (PCA) was widely used as a multivariate statistical and exploratory analysis methodology to interpret many-variable data matrix. PCA is widely used to explain the processes which influence groundwater quality by examining chemical associations defined by one or more variable loadings on factors (Chen and Jiao 2007;Souid et al 2018;Bi et al 2021;Zakhem et al 2017;Shirnezhad et al 2020;Ju and Hu 2021). The PCA is based on the reduction of large variables in the initial matrix and the construction of new ones named components.…”
Section: Statistical Methodologiesmentioning
confidence: 99%