Mine Water and the Environment

, Volume 38, Issue 4, pp 855–862 | Cite as

Combining the Fisher Feature Extraction and Support Vector Machine Methods to Identify the Water Inrush Source: A Case Study of the Wuhai Mining Area

  • Donglin Dong
  • Zhiyuan Chen
  • Gang LinEmail author
  • Xiang LiEmail author
  • Ruomeng Zhang
  • Yuan Ji
Technical Communication


Discriminating the source of water inrush accurately and efficiently is necessary for water control in the coal mining industry. We combined the Fisher feature extraction and support vector machine (SVM) methods and applied this new model to the Wuhai mining area. The method extracts features from the raw data and integrated SVM, and synthetically considers the influence of geographical factors. Cross-analysis was tested 100 times, which arbitrarily selected 12 samples for the prediction and discrimination process. The results indicate that this new combined model of linear dimension reduction and non-linear dimension elevation was more accurate and efficient in discriminating water inrush sources than the traditional SVM model. Moreover, by reducing the penalty term of SVM model, we analyzed the correlation among the aquifers. We concluded that aquifers II and IV correlated strongly with each other, and that aquifer III was poorly connected with the other aquifers.


Mine water-inrush source Intelligent recognition Coal mine 

Identifizierung der Wassereinbruchquelle mittels Kombination von Fisher-Merkmalextraktion und unterstützenden Vektormaschinen: Fallstudie im Wuhai-Bergbaugebiet


In der Kohlenbergbauindustrie ist die präzise und effiziente Unterscheidung der Quelle von Wassereinbrüchen zur Kontrolle erforderlich. Wir kombinierten die Fisher-Merkmalextraktion (FFE) und die Methode unterstützender Vektormaschinen (SVM) und verwendeten das neue Modell im Wuhai-Bergbaugebiet. Die Methode extrahiert Merkmale aus Rohdaten und integrierten Vektormaschinen und bezieht den Einfluß geographischer Faktoren synthetisierend ein. Queranalysen wurden 100-fach getestet, daraus wurden 12 zufällig gewählte Proben für den Prozess der Vorhersage und Unterscheidung entnommen. Die Resultate zeigen, daß dieses neue kombinierte Modell der Reduktion linearer Dimensionen und nicht-linearer Höhendimension bei der Unterscheidung von Einbruchquellen genauer und effizienter war als das traditionelle SVM-Modell. Durch Reduktion des Strafterms des SVM-Modelles untersuchten wir die Korrelation der Aquifere. Wir schlossen daraus, daß die Aquifere I und IV stark miteinander korrelierten, während Aquifer III mit den anderen Aquiferen gering verbunden war.

Combinación de los métodos de extracción de características Fisher y de máquina de vectores de extracción para identificar la fuente de irrupción de agua: un estudio de caso del área minera de Wuhai


Para el control de la irrupción de agua en la industria minera del carbón, es necesario discriminar la fuente de entrada de agua de forma precisa y eficiente. Hemos combinado los métodos de extracción de características Fisher (FFE) y de máquina de vectores de soporte (SVM) para aplicarlo posteriormente al área minera de Wuhai. El método extrae características de los datos en bruto y SVM integrado y considera sintéticamente la influencia de factores geográficos. El análisis cruzado se probó 100 veces, que seleccionó arbitrariamente 12 muestras para el proceso de predicción y discriminación. Los resultados indican que este nuevo modelo combinado de reducción de dimensión lineal y elevación de dimensión no lineal fue más preciso y eficiente para discriminar las fuentes de entrada de agua que el modelo SVM tradicional. Además, al reducir el término de penalización del modelo SVM, analizamos la correlación entre los acuíferos. Llegamos a la conclusión de que los acuíferos I y IV se correlacionaban fuertemente entre sí y que el acuífero III estaba pobremente conectado con los otros acuíferos.

Fisher特征提取与支持微量机(SVM) 相结合的矿井突水水源判别方法:以乌海矿为例





This study was financially supported by the Ministry of Science and Technology of China (2017YFC0804104), the Consultation Project of Chinese Academy of Engineering (2017-ZD-03-05-01), the National Natural Science Foundation (U1710258), and the China Postdoctoral Science Foundation (2018T110134).

Supplementary material

10230_2019_637_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 24 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Geological Engineering and EnvironmentChina University of Mining and Technology, Beijing (CUMTB)BeijingChina

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