The purpose of this research was to understand the hydrogeochemical characteristics and assess the quality of phreatic and confined groundwater in southern Hebei Province. A total of 107 groundwater samples were collected, representing different aquifer conditions over the study area. Multivariate statistical analysis, hydrochemical maps, ionic ratio coefficients, geographic information system (GIS) and geochemical simulation were comprehensively and systematically used to reveal the hydrochemical characteristics of groundwater and its controlling mechanism. The results revealed that both phreatic (pH = 7.02–9.08) and confined groundwater (pH = 7.00–10.60) were slightly alkaline. The hydrochemical types were mainly present as the HCO₃-Ca-Mg type in the western premontane area and mixed Ca-Mg-SO₄-Cl and Na-Cl-SO₄ types in the eastern plains. The hydrochemical composition was dominated by water–rock interactions of natural processes, including silicate weathering, dissolution of sulfate minerals (gypsum, anhydrite), and cation-exchange adsorption. Anthropogenic activities were the main factor causing NO₃⁻ content in some groundwater samples to exceed the geochemical baseline. The hydrogeochemistry of groundwater in different aquifers was significantly varied. The average contents of TH, TDS, Na⁺, Ca²⁺, Mg²⁺, Cl⁻ and SO₄²⁻ in phreatic aquifers were significantly higher than those in confined aquifers. The Entropy Weighted Water Quality Index (EWQI) results revealed that 17.78% of phreatic and 50% of confined water samples were meeting the purpose of drinking water. The groundwater samples with EWQI values exceeding 100 were mainly situated in the Handan urban area and the eastern region of Xingtai City, which should be avoided for direct utilization and needs to be improved through protection and management measures, to enhance the quality of groundwater. Correlation analysis showed that groundwater quality was significantly dominated by TH, TDS, Na⁺, Mg²⁺, Cl⁻ and SO₄²⁻ concentrations.

Mine water hazards are one of the main factors limiting the safe and efficient extraction of coal resources in China. The rapid and accurate identification of the source of water inrushes is central to the prevention and control of mine water hazards. This study aims to address the issues of complex pre-treatment procedures and low accuracy in identifying mixed water samples associated with traditional methods for determining the source of mine water inrushes. This study proposes an intelligent model for identifying the source of mine water inrushes that integrates ultraviolet–visible spectrophotometry (UV–Vis) with convolutional neural networks (CNNs). This model utilizes convolutional neural networks to automatically extract features from and classify spectral images of water sources associated with mine water inrushes, eliminating the cumbersome pre-processing steps involved in traditional spectral source analysis and significantly improving the efficiency of water source identification. The CNN is used to classify and identify the mine water spectral images measured by UV–Vis. The model training results showed that the accuracy of the model was 98.89%, 95.93%, and 90.67% for the training sets of single water samples, mixed water samples, and overall water samples, respectively. The test results indicated that the model was accurate for all the water samples in the test set, and the UV-CNN model had better classification recognition ability for complex mixed water samples compared with the water chemistry-based kernel function principal component analysis and support vector machine (KPCA-SVM) discrimination model. When the characteristics of water samples are relatively similar, the discriminative advantage of the model becomes more prominent. This model has the features of fast convergence speed, short computing time, high discriminative accuracy and stability, and can provide an efficient technical method for the rapid and accurate identification of the water source of mine water inrush.

Accurate prediction of aquifer water abundance is critical for coal mine safety, yet traditional static models often fail to capture the spatial heterogeneity and non-stationarity of hydrogeological conditions. This study proposes a dynamic evaluation methodology integrating Grey Relational Analysis, the Analytic Hierarchy Process, and a Time-Varying Parameter Cubature Kalman Filter (TVP-CKF). By reconceptualizing spatial borehole data as a dynamic time-series process, the model recursively updates the contribution weights of six controlling factors based on monitoring data from 2012 to 2020. Analysis reveals a structural shift in the groundwater system: the influence of hydrochemical factors (TDS) has diminished, while hydraulic conductivity has become the dominant control over time. The TVP-CKF model significantly outperformed static regression and recursive least squares baselines, demonstrating superior convergence stability and precisely capturing transient inflow fluctuations. Furthermore, its uncertainty quantification effectively bounded extreme low-flow events within 95% confidence intervals. This approach validates the necessity of adaptive modeling in evolving geological environments, providing a robust, risk-quantified tool for precise water inrush prevention.