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 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.
Highlights
Coal resource is an important basic resource for the longterm rapid and stable development of the national economy in China [1]
The reason is that partial least squares-discriminate analysis (PLS-DA) is a supervised discriminant analysis method, which artificially adds grouping variables, further excavates the information in the water sample data, strengthens the difference of water chemical composition between different water sources, and makes up for the deficiency of the principal component analysis (PCA) method [28]
Compared with the PCA method, partial least squares (PLS)-DA has the function of quantifying the degree of difference between different water sources caused by the characteristic water chemical composition
Summary
Coal resource is an important basic resource for the longterm rapid and stable development of the national economy in China [1]. As mining depth has increased, water inrush disaster occurrence has increased, which poses a serious threat to the safety of coal mine production [2, 3]. And accurately identifying the sources of mine water inrush is important for the prevention and control of coal mine water inrush disasters, and it is a top concern in mine water disaster management research [4]. After comparing groundwater chemistry with other methods, Wu et al [12] concluded that the water chemistry discrimination method had more advantages in practical applications. Because groundwater chemistry can reflect the essential characteristics of groundwater, and can Geofluids accurately, quickly, and economically identify water sources, it has been more commonly used to identify water inrush water sources in mines [13]
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