scholarly journals Identification of Mine Water Inrush Source Based on PCA-FDA: Xiandewang Coal Mine Case

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Li ◽  
Qiang Wu ◽  
Zijie Liu
Keyword(s):  
Chemical Composition ◽  
Coal Mine ◽  
Mine Water ◽  
Source Identification ◽  
Water Inrush ◽  
Water Sources ◽  
Identification Accuracy ◽  
Fisher Discriminant Analysis ◽  
Water Chemical Composition ◽  
Identification Model

When mine water inrush accidents occur, timely and accurately identifying the water inrush source plays an important role in determining the cause of water inrush and making a solution to a disaster. According to the differences of water chemical composition in each water sources of mine, eight kinds of indicators of water chemical composition were selected as sample variables for water inrush source identification. On this basis, an identification model of water inrush source was established by using principal component analysis (PCA) and Fisher discriminant analysis (FDA) combined. The model was used to identify the water inrush source of 14 groups of training samples and 12 groups of samples to be judged in different water sources of the Xiandewang coal mine, and it was compared with the results of the conventional identification model which used the FDA method. Results of this study showed that having processed data by using the PCA method can effectively eliminate the effects of information superposition between sample indicators, and the identification accuracy of mine water inrush source was significantly increased. Related study in this paper can provide some basis and reference for the study of mine water inrush source identification technology.

scholarly journals Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yaoshan Bi ◽  
Jiwen Wu ◽  
Xiaorong Zhai ◽  
Shuhao Shen ◽  
Libin Tang ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Water Samples ◽  
Mine Water ◽  
Water Inrush ◽  
Chemical Compositions ◽  
Discriminate Analysis ◽  
Discriminant Model ◽  
Water Chemical Composition ◽  
Multiple Aquifers ◽  
Different Water Sources

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.

Identification Method of Coal Mine Water Inrush Spectrum Based on Multilayer Regularization Extreme Learning Machine

2018 ◽  
Vol 38 (7) ◽  
pp. 0730002
Author(s):  
王亚 Wang Ya ◽  
周孟然 Zhou Mengran ◽  
陈瑞云 Chen Ruiyun ◽  
闫鹏程 Yan Pengcheng ◽  
胡锋 Hu Feng ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Coal Mine ◽  
Mine Water ◽  
Water Inrush ◽  
Identification Method ◽  
Coal Mine Water ◽  
Learning Machine

Rapid identification model of mine water inrush sources based on extreme learning machine

2017 ◽  
Vol 13 (4) ◽  
pp. 286 ◽  
Author(s):  
Ya Wang ◽  
Mengran Zhou ◽  
Pengcheng Yan ◽  
Feng Hu ◽  
Wenhao Lai ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Mine Water ◽  
Water Inrush ◽  
Rapid Identification ◽  
Learning Machine ◽  
Identification Model

Discrimination of Mine Water Bursting Source Based on Fuzzy System

2012 ◽  
Vol 182-183 ◽  
pp. 644-648
Author(s):  
Wei Feng Yang ◽  
Ding Yi Shen ◽  
Yu Bing Ji ◽  
Yi Wang
Keyword(s):  
Coal Mine ◽  
Mine Water ◽  
Fuzzy System ◽  
Comprehensive Evaluation ◽  
Water Inrush ◽  
Fuzzy Comprehensive Evaluation ◽  
Mining Area ◽  
Shanxi Province ◽  
Estimation Model ◽  
Water Bursting

Through applying the background values of aquifer derived from fuzzy clustering analysis, a fuzzy comprehensive estimation model was developed for quick recognition of mine water inrush. Based on the hydrological-chemical analysis data of water samples which water bursting sources were known in Liliu mining area, Shanxi province, this paper presented that the hydrological-chemical characters of different aquifer was different, and established a sort of fuzzy comprehensive evaluation models of discriminating coal mine water bursting sources in Liliu mining area. Applied to a production mine, the correct rate of water bursting source judged results by various methods was more than 70%. With the dispersion method and the method extracted from stepwise discrimination analysis to determine the membership degree and Model 3 the type determined by various factors, the correct rate of water bursting source with comprehensive evaluation of combination of two methods was higher respectively 94.5% and 93.3%. The fuzzy system can efficiently and accurately discriminate the resource of water inrush for an unknown sample, and provide the decision basis for the safety production of the coal mine.

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