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An attribute recognition model to predict the groundwater potential of sandstone aquifers in coal mines

  • Shou-Qiao Shi
  • Jiu-Chuan WeiEmail author
  • Dao-Lei Xie
  • Hui-Yong Yin
  • Wei-Jie Zhang
  • Li-Yao Li
Article
  • 6 Downloads

Abstract

The groundwater potential prediction of sandstone aquifers is an important pre-requisite for the implementation of reasonable and effective measures to prevent mine water inrush disasters. In this study, an attribute recognition model was combined with entropy weighting to predict the groundwater potential of sandstone aquifers in coal mines. Five evaluation indices were selected to predict groundwater potential, such as sandstone thickness, sandstone lithology coefficient, flushing fluid consumption, fracture fractal dimension and fold fractal dimension. On the basis of data analysis, the groundwater potential was classified into four levels. Confidence and improved score criteria were applied to attribute recognition. The main advantages of this model are that it enables both the prediction and quantification of the groundwater potential of sandstone aquifers. The model’s results were compared with those from a comprehensive geographic information system evaluation. The final model results were in good agreement with the observed results, proving that this attribute recognition model is accurate and effective for groundwater potential prediction.

Keywords

Groundwater potential attribute recognition confidence criterion improved score criterion 

Notes

Acknowledgements

This study was supported by the National Nature Science Foundation of China (Grant Nos. 41372290 and 41402250) and the Nature Science Foundation of Shandong Province (Grant No. ZR2015PD010).

Supplementary material

12040_2019_1100_MOESM1_ESM.pdf (567 kb)
Supplementary material 1 (pdf 566 KB)
12040_2019_1100_MOESM2_ESM.pdf (611 kb)
Supplementary material 2 (pdf 611 KB)

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Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.College of Earth Sciences and EngineeringShandong University of Science and TechnologyQingdaoPeople’s Republic of China

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