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

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

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

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

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.

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