A combination of geographic information system (GIS) and spatial multicriteria decision making (MCDA) in mine water inrush risk evaluation is widely used, but the randomness in the process of index weight determination and the risk-coping attitude of decision makers are not considered in the decision making process. Therefore, this paper proposes a probability-based roof water inrush risk evaluation method (GIS-MCDA) by combining the Monte Carlo analytic hierarchy process (MAHP) and ordered weighted averaging (OWA) operator. This method uses MAHP to determine the weight of the evaluation indicators, reducing the randomness of the analytic hierarchy process (AHP) to determine the weight of the evaluation indicators using the OWA operator to quantify the five risk-coping attitudes of decision makers and incorporate the risk attitude of decision makers into the evaluation process. Taking the Liangshuijing Coal Mine in northern Shaanxi as an example, the application of the GIS-MCDA method showed that the method makes the risk results of roof water inrush more objective and comprehensive and reduces or avoids the risk of decision making due to human subjective tendency change.

Most of the existing coal mine water inrush risk assessment methods are global assessment methods, which have the following problems: they ignore the difference in importance of the evaluation indicators at different locations in the study area and assign the same weight value; the area of the danger zone in the evaluation results is thus too large. The evaluation results improve the prediction accuracy by reducing the safe zone area. To address the aforementioned issues, this study employs a local analysis method based on a moving circular window, taking into account the spatial heterogeneity of criterion indicators in the decision-making process. By traversing each position of the raster with a circular moving window, the method performs local standardization and calculates local weights of indicators within the local window range. Based on the obtained maps of locally standardized evaluation criteria and local weights, a local water inrush risk assessment model is established using Geographic Information Systems (ArcGIS), considering the differences in the importance of evaluation indicators within the study area. Taking the Liangshuijing mining area as an example, both global and local evaluation models were employed to assess its water inrush risk. The evaluation results obtained from these two models were compared and validated against geological survey data and historical water inrush points. The comparative analysis between the two methods reveals that the local evaluation model demonstrates higher accuracy. It offers a more precise delineation of the distribution of water inrush risk zones, which better corresponds to the actual conditions within the mine. The localized water inrush risk assessment method proposed in this paper breaks away from the traditional approach of uniformly weighting evaluation indicators across the entire area, offering a novel method for assessing water inrush risk.

After coal mine wastewater is artificially injected into a groundwater reservoir transformed from a goaf, micron-sized suspended matter in the wastewater is purified by the broken rock mass in the goaf. Existing studies can only analyze the macroscopic changes in the content of suspended solids during the purification process, and it is difficult to explain the microscopic deposition mechanism of the suspended solids in broken rock. This paper studied the microscopic deposition behavior of micron-sized suspended solids inside the broken rock mass via numerical simulation using a coupled CFD-DEM method. In addition, indoor model tests were carried out to verify the accuracy and reliability of the model in comparison. The study results show that suspended solids’ deposition behavior varies significantly under broken rock masses’ different pore sizes (0.47 mm, 1.14 mm, 3.00 mm, and 5.33 mm). Within the goaf, the adsorption of suspended solids by the broken rock mass plays a dominant role. At the same time, suspended particles are mostly collected in the inlet area, and the difference in the number of deposited particles can reach 74% when comparing the first 50 mm range as well as the 50–100 mm range. The number of deposited particles at a flow rate of 0.02 m/s is 14% more than that at a flow rate of 0.06 m/s. This work offers new ideas for studying the purification mechanism of coal mine wastewater within a goaf.

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations.

In recent years, with the continuous increase in coal mining depth and intensity, hydrogeological conditions in coal mines have become increasingly complex, and the risk of floor water inrush has risen significantly. To address the limitations of the global weighting pattern in traditional floor water inrush risk evaluation systems, this study, taking a coal mine in Shaanxi Province as a case, develops a local water inrush risk evaluation method based on the Monte Carlo Analytic Hierarchy Process (MAHP) combined with a circular moving window, and compares it with the water inrush coefficient method and the global evaluation method. The results demonstrate that the proposed local evaluation model achieves higher accuracy, provides a more refined delineation of high-risk zones, and shows stronger consistency with actual mining conditions. Further comparison of window radii of 100 m, 500 m, and 900 m indicates that the 500 m radius performs best in terms of spatial morphology, area proportion, and water inrush point identification rate (89.3%). Moreover, its application in Yangcheng Coal Mine further confirms that this method can accurately identify high-risk zones, thereby offering reliable scientific support for the prevention and control of coal seam floor water inrush.

Mine water hazards represent one of the principal threats to safe coal mine operations; therefore, accurately predicting mine water inflow is critical for drainage system design and water hazard mitigation. Because mine water inflow is governed by the combined influence of multiple hydrogeological factors and thus exhibits pronounced non-linear characteristics, conventional approaches are inadequate in terms of forecasting accuracy and medium- to long-term predictive capability. To address this issue, this study proposes a Chaos-Autoformer-based method for predicting mine water inflow. First, the univariate inflow series is mapped into an m-dimensional phase space by means of phase-space reconstruction from chaos theory, thereby fully preserving its non-linear features; the reconstructed vectors are then used to train and forecast inflow with an improved Chaos-Autoformer model. On top of the original Autoformer architecture, the proposed model incorporates a Chaos-Attention mechanism and a Lyap-Dropout scheme, which enhance sensitivity to small perturbations in initial conditions and complex non-linear propagation paths while improving stability in long-horizon forecasting. In addition, the loss function integrates the maximum Lyapunov exponent error and earth mode decomposition (EMD) indices so as to jointly evaluate dynamical consistency and predictive performance. An empirical analysis based on monitoring data from the Liangshuijing Coal Mine for 2022–2025 demonstrates that the trained model delivers high accuracy and stable performance. Ablation experiments further confirm the significant contribution of the chaos-aware components: when these modules are removed, forecasting accuracy declines to only 76.5%. Using the trained model to predict mine water inflow for the period from June 2024 to June 2025 yields a root mean square error (RMSE) of 30.73 m³/h and a coefficient of determination (R²) of 0.895 against observed data, indicating excellent fitting and predictive capability for medium- to long-term tasks. Extending the forecast to July 2025–November 2027 reveals a pronounced annual cyclical pattern in future mine water inflow, with markedly higher inflow in summer than in winter and an overall slowly declining trend. These findings show that the Chaos-Autoformer can achieve high-precision medium- and long-term predictions of mine water inflow, thereby providing technical support for proactive deployment and refined management of mine water hazard prevention.