Application of entropy weight-variable fuzzy set theory in water-inrush source identification of multiple aquifers in deep coal mine

Accurate identification of water-inrush sources is critical for deep mining safety. This study proposes an entropy-weighted variable fuzzy set (EW-VFS) model, which uses information entropy to objectively determine the importance of different hydrochemical indicators, to discriminate complex mixed water sources in the Sunzhuang Minefield, North China. In this study, we analyzed 86 water samples from five key aquifers—Permian sandstone fractured aquifers, Ordovician limestone karst aquifers, and three thin-layer limestone aquifers—using nine hydrochemical parameters. Entropy weight analysis identified Mg² + and HCO 3 − as the dominant indicators for source discrimination. The EW-VFS model achieved an overall accuracy of 83.33%, demonstrating high reliability, particularly for Permian sandstone fractured water and Ordovician limestone water. Furthermore, a time-series analysis of the model's rank feature value ( H i ) revealed a dynamic evolution of the inrush source, showing a clear transition between different thin-layer limestone aquifers during mining operations. This study's findings demonstrate the model's utility in identifying multisource water inrush. However, its performance in differentiating the highly similar thin-layer limestone aquifers shows potential for further enhancement, which could be addressed with more comprehensive hydrogeochemical data.