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

Abstract

The groundwater potential prediction of sandstone aquifers is an important prerequisite 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.