Assessment of water inrush risk using the principal component logistic regression model in the Pandao coal mine, China

  • Weitao Liu
  • Qiang LiEmail author
  • Jiyuan ZhaoEmail author
  • Biao Fu
Original Paper


In order to improve the accuracy of floor water inrush assessment, the risk prediction model of floor water inrush was established by combining the principal component logistic regression analysis (PCLRA) and GIS spatial geographic analysis. In this paper, the geological data of Pandao coal mine was taken as the engineering background. First of all, main controlling factors of floor water inrush were determined and quantified. Next, PCLRA was used to determine the weight of each factor and establish the mathematical model for predicting the floor water inrush. And then, GIS’s spatial analysis and data processing function was used to draw related single factor thematic maps. Related thematic maps were weighted superposed to draw a floor water inrush zoning map based on PCLRA mathematical model. The study areas were divided into five levels by Jenks optimization method and vulnerability index initial model. And the corresponding threshold range was determined. The results show that (1) the high sensitivity factors in floor failure depth were added to evaluate the water inrush, and the fault fractal dimension was used to replace the fault structure related factors, and the main controlling factors of floor water inrush are more comprehensive; (2) the fitting degree of PCLRA model is high and the test accuracy is 83.3%; (3) the prediction results were well fitted to the actual position of water inrush (three water inrush points are located in the dangerous area, and two water inrush points are located in the relatively dangerous area).


Floor failure depth Water inrush prediction model Vulnerability index model Floor water inrush risk assessment 


Funding information

This work was supported by the National Natural science Foundation of China (Grant 51274135), the National High Technology Research and Development Program (863 Program) of China (Grant 2015AA016404-4), and the State Key Research and Development Program of China (Grant 2017YFC0804108).


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Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.Shandong University of Science and TechnologyQingdaoChina

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