In recent years, the groundwater level (GWL) and its dynamic changes in the Hebei Plain have gained increasing interest. The GWL serves as a crucial indicator of the health of groundwater resources, and accurately predicting the GWL is vital to prevent its overexploitation and the loss of water quality and land subsidence. Here, we utilized data-driven models, such as the support vector machine, long-short term memory, multi-layer perceptron, and gated recurrent unit models, to predict GWL. Additionally, data from six GWL monitoring stations from 2018 to 2020, covering dynamical fluctuations, increases, and decreases in GWL, were used. Further, the first 70% and remaining 30% of the time-series data were used to train and test the model, respectively. Each model was quantitatively evaluated using the root mean square error (RMSE), coefficient of determination (R²), and Nash–Sutcliffe efficiency (NSE), and they were qualitatively evaluated using time-series line plots, scatter plots, and Taylor diagrams. A comparison of the models revealed that the RMSE, R², and NSE of the GRU model in the training and testing periods were better than those of the other models at most groundwater monitoring stations. In conclusion, the GRU model performed best and could support dynamic predictions of GWL in the Hebei Plain.

Water resources are scarce in arid or semiarid areas; groundwater is an important water source to maintain residents’ lives and the social economy; and identifying the hydrogeochemical characteristics of groundwater and its seasonal changes is a prerequisite for sustainable use and protection of groundwater. This study takes the Hongjiannao Basin as an example, and the Piper diagram, the Gibbs diagram, the Gaillardet diagram, the Chlor-alkali index, the saturation index, and the ion ratio were used to analyze the hydrogeochemical characteristics of groundwater. Meanwhile, based on self-organizing maps (SOM), quantification error (QE), topological error (TE), and the K-means algorithm, groundwater chemical data analysis was carried out to explore its seasonal variability. The results show that (1) the formation of groundwater chemistry in the study area was controlled by water–rock interactions and cation exchange, and the hydrochemical facies were HCO₃-Ca type, HCO₃-Na type, and Cl-Na type. (2) Groundwater chemical composition was mainly controlled by silicate weathering and carbonate dissolution, and the dissolution of halite, gypsum, and fluorite dominated the contribution of ions, while most dolomite and calcite were in a precipitated state or were reactive minerals. (3) All groundwater samples in wet and dry seasons were divided into five clusters, and the hydrochemical facies of clusters 1, 2, and 3 were HCO₃-Ca type; cluster 4 was HCO₃-Na type; and cluster 5 was Cl-Na type. (4) Thirty samples changed in the same clusters, and the groundwater chemistry characteristics of nine samples showed obvious seasonal variability, while the seasonal changes of groundwater hydrogeochemical characteristics were not significant.

The over-extraction of groundwater has resulted in seawater intrusion and the southward migration of the saltwater interface, gradually deteriorating the groundwater quality in the Weibei Plain. In this research, groundwater samples were gathered from 46 monitoring wells for shallow groundwater during the years 2006, 2011, 2016, and 2021. The hydrochemical features of regional groundwater and the factors influencing the issue were subjected to statistical analysis. Additionally, the assessment of spatiotemporal variations in groundwater quality was conducted using the customized entropy-weighted water quality index (EWQI) method. The relationship between groundwater over-extraction and the southward intrusion of the saltwater interface was compared and analyzed. The results of this paper revealed that the Weibei Plain has been in a state of long-term over-extraction of groundwater from 2000 to 2021, with an average annual over-extraction of 118.49 million m³. The groundwater depression cone areas in the northern part of the study area increased from 3,247.37 to 4,581.34 km² from 2006 to 2021, with the center of the cone experiencing a drop in groundwater level from −22 to −85 m. The saltwater interface shifted southward by 711.71 km² from 2006 to 2021. In groundwater, the high concentrations of TH, TDS, and Cl⁻ were primarily related to the seawater intrusion, while higher concentrations of NO₃⁻ were mainly determined by frequent agricultural production, industrial wastewater, and domestic sewage discharges. The groundwater hydrochemical types in the study area transitioned from predominantly HCO₃·Ca-Mg type in 2006 to HCO₃-Na type and SO₄·Cl-Ca·Mg type in 2021 due to seawater intrusion. The results of PCA and HCA show the effects of seawater intrusion, human activities, and rock weathering on groundwater hydrochemistry. The evaluation results based on the EWQI revealed that the average value of the samples in 2021 was 101.36, which belonged to Class IV water quality standards, representing the poorest water quality among the 4 years. The southward migration of the saltwater interface led to the deterioration of groundwater quality in the groundwater depression cone areas, which gradually worsened from 2006 to 2021. The maximum increase in EWQI value was 174.68 during the period, shifting from Class III water quality to Class V water quality. Groundwater quality remained relatively better in the western and southern regions which were less affected or unaffected by seawater intrusion. The results of the study can provide a certain reference value for the sustainable management of groundwater resources and the management of groundwater pollution and seawater intrusion in the Weifang City area in the future.

This study aims to improve the accuracy and interpretability of deep groundwater level forecasting in Cangzhou, a typical overexploitation area in the North China Plain. To address the limitations of traditional models and existing machine learning approaches, we develop a Stacking ensemble learning framework that integrates meteorological, spatial, and anthropogenic variables, including lagged groundwater levels to reflect aquifer memory. The model combines six heterogeneous base learners with a meta-model to enhance prediction robustness. Performance evaluation shows that the ensemble model consistently outperforms individual models in accuracy, generalization, and spatial adaptability. Scenario-based simulations are further conducted to assess the effects of the South-to-North Water Diversion Project. Results indicate that the diversion project significantly mitigates groundwater depletion, with the most overexploited zones showing water level recovery of up to 17 m compared to the no-diversion scenario. Feature importance analysis confirms that lagged water levels and pumping volumes are dominant predictors, aligning with groundwater system dynamics. These findings demonstrate the effectiveness of ensemble learning in modeling complex groundwater behavior and provide a practical tool for water resource regulation. The proposed framework is adaptable to other groundwater-stressed regions and supports dynamic policy design for sustainable groundwater management.