Land use/land cover (LULC) significantly affects groundwater quality. This study analyzed the spatial distribution characteristics of groundwater hydrochemical parameters in the Guanzhong Basin and assessed the relationships between the groundwater parameters and LULC using statistical models and a curved streamline searchlight-shaped model (CS-SLM). The results showed that higher groundwater parameter concentrations were observed in the north of the plain than in the south. Forest and water bodies showed a positive effect on most hydrochemical parameters (Na⁺, Cl⁻, SO₄²⁻, F⁻, Cr⁶⁺), while bare land and cropland revealed a negative effect on them. In addition, the Patch-generating Land Use Simulation (PLUS) model was used to predict the LULC patterns for 2030 and to qualitatively analyze the potential changes in groundwater quality associated with LULC changes. The simulation results highlighted a significant decrease in forest areas in the southern part of Guanzhong Plain by 2030, resulting in potential groundwater quality deterioration.

Groundwater pollution is a serious issue in arid and semi-arid regions. In this study, the ordinary least squares (OLS) regression and geographically weighted regression (GWR) model were used to assess the relationship between hydrochemical parameters (NO₃-N, NO₂-N, NH₄-N, and F^-) and explanatory variables related to anthropogenic and natural factors, including elevation, slope, population density, groundwater electrical conductivity, groundwater pH, and land use in the Weinan region of China. The results showed that NO₃-N, NH₄-N, and F^- at 24, 4, and 54% of the samples exceeded the standard limits, respectively. Crop fields, grassland, and forest are the most common land use types in the study area, accounting for 62.84, 16.77, and 8.76%, respectively. The effects of explanatory variables on groundwater quality show strong spatial variation. Both positive and negative correlations were observed between groundwater nitrogen (NO₂-N, NO₃-N) and orchard, and between F^- and crop field. The water area has significant impacts on NH₄-N in Pucheng, Fuping and Linwei districts. The GWR model also suggested significant effects of water and orchard areas on groundwater NO₂-N concentration in western Fuping County and eastern Dali County, which was neglected by the OLS model. The research shows advantages of the GWR model in capturing local variation.

As hydraulic buildings are constantly subjected to complex interactions with water, particularly variations in hydrostatic pressure and temperature, deformation structural behavior is inherently sensitive to environmental fluctuations. Monitoring dam deformation with high accuracy and robustness is critical for ensuring the long-term safety and operational integrity of hydraulic structures. However, traditional physics-based models often struggle to fully capture the nonlinear and time-dependent deformation responses in hydraulic structures driven by such coupled environmental influences. To address these limitations, this study presents an advanced deep learning (DL)-based deformation monitoring for hydraulic buildings using long-sequence monitoring data of hydrostatic pressure and temperature. Specifically, the Bidirectional Stacked Long Short-Term Memory (Bi-Stacked-LSTM) is proposed to capture intricate temporal dependencies and directional dynamics within long-sequence hydrostatic and thermal time series. Then, hyperparameters, including the number of LSTM layers, neuron counts in each layer, dropout rate, and time steps, are efficiently fine-tuned using the Gaussian Process-based surrogate model optimization (GP-SMO) algorithm. Multiple deformation monitoring points from hydraulic buildings and a variety of advanced machine-learning methods are utilized for analysis. Experimental results indicate that the developed GP-SMO-optimized Bi-Stacked-LSTM dam deformation monitoring model shows better comprehensive representation capability of both past and future deformation-related sequences compared with benchmark methods. By approximating the behavior of the target function, the GP-SMO algorithms allow for the optimization of critical parameters in DL models while minimizing the high computational costs typically associated with direct evaluations. This novel DL-based approach significantly improves the extraction of deformation-relevant features from long-term monitoring data, enabling more accurate modeling of temporal dynamics. As a result, the developed method offers a promising new tool for safety monitoring and intelligent management of large-scale hydraulic structures.