Hidden disaster-causing factor investigation is a fundamental task for safety production in mines. Water hazards in karst metal underground mines are characterized by complex disaster-forming mechanisms, strong suddenness, and high risk, while traditional assessment methods are prone to expert subjective bias and cannot meet the demand for precise prevention and control. This study proposes an improved fuzzy comprehensive evaluation model by integrating the analytic hierarchy process (AHP) and normal distribution-based expert confidence weighting. A three-level assessment index system consisting of 3 first-level indicators and 11 s-level indicators is established for karst metal mine water hazard risk. The normal distribution function is used to quantify expert confidence weights so as to reduce subjective deviation. A three-level fuzzy comprehensive evaluation is performed to achieve quantitative risk grading, and the model robustness is verified through sensitivity analysis. Furthermore, three-dimensional geological modeling and seepage–stress coupling numerical simulation are conducted using COMSOL 6.0 software to validate the reliability of assessment results. The Mao’erling Gold Mine in Hunan Province is taken as a case study. The evaluation yields a comprehensive membership vector of (0.103, 0.130, 0.184, 0.351, 0.232), which is strongly consistent with numerical simulation results and field water inrush records. The results demonstrate that the improved model features strong objectivity and favorable robustness, and can provide a scientific basis for water hazard investigation, risk assessment, and prevention engineering in karst metal underground mines.
Liu et al. (2026) studied this question.