Aquifer health assessment is essential for sustainable groundwater management, particularly in semi-arid regions with challenging geological conditions. This study presents a novel methodology for assessing aquifer health in the Barakar River Basin, a hard-rock terrain, by integrating tree-based classification, deep learning, and the Soil and Water Assessment Tool (SWAT) model. Employing Random Forest, Decision Tree, and Convolutional Neural Network (CNN) models, the research examines 20 influential factors, including hydrological, water quality, and socioeconomic variables, to classify aquifer health into four categories: Good, Moderately Good, Semi-Critical, and Critical. The CNN model exhibited the highest predictive accuracy, identifying 33% of the basin as having good aquifer health, while Random Forest assessed 27% as Critical heath. Pearson correlation analysis of CNN-predicted aquifer health indicates that groundwater recharge (r = 0.52), return flow (r = 0.50), and groundwater fluctuation (r = 0.48) are the most influential positive factors. Validation results showed that the CNN model performed strongly, with a precision of 0.957, Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) of 0.95, and F1 score of 0.828, underscoring its reliability and robustness. Geophysical Electrical Resistivity Tomography (ERT) field surveys validated these classifications, particularly in high- and low-aquifer health zones. This study enhances understanding of aquifer dynamics and presents a robust methodology with broader applicability for sustainable groundwater management worldwide.

This study provides an in-depth analysis of time series forecasting methods to predict the time-dependent deformation trend (also known as creep) of salt rock under varying confining pressure conditions. Creep deformation assessment is essential for designing and operating underground storage facilities for nuclear waste, hydrogen energy, or radioactive materials. Salt rocks, known for their mechanical properties like low porosity, low permeability, high ductility, and exceptional creep and self-healing capacities, were examined using multi-stage triaxial creep data. After resampling, axial strain datasets were recorded at 5–10 sec intervals under confining pressure levels ranging from 5 to 35 MPa over 5.8–21 days. Initial analyses, including Seasonal-Trend Decomposition and Granger causality tests, revealed minimal seasonality and causality between axial strain and temperature data. Further statistical tests, such as the Automated Dickey–Fuller test, confirmed the stationarity of the data with p-values less than 0.05, and wavelet coherence plot analysis indicated repeating trends. A suite of deep neural network (DNN) models—Neural Basis Expansion Analysis for Time Series (N-BEATS), Temporal Convolutional Networks (TCN), Recurrent Neural Networks, and Transformers—were utilized and compared against statistical baseline models. Predictive performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE in %), and Symmetric Mean Absolute Percentage Error (SMAPE in %). Results demonstrated that N-BEATS and TCN models outperformed others, with RMSE values of(0.3325–1.257) and 0.540–1.352, MAE from 0.287–0.961 and 0.472–1.177, MAPE from 1.45–4.54 and 2.85–6.28, and SMAPE from 1.46–4.62 and 2.88–6.03 across various stress levels, respectively. DNN models, particularly N-BEATS and TCN, showed a 15% improvement in accuracy over traditional analytical models, effectively capturing complex temporal dependencies and patterns. This research significantly advances time series forecasting in geosciences, offering crucial insights for the safe and efficient management of underground storage in rock salt formations.

This study provides an in-depth analysis of time series forecasting methods to predict the time-dependent deformation trend (also known as creep) of salt rock under varying confining pressure conditions. Creep deformation assessment is essential for designing and operating underground storage facilities for nuclear waste, hydrogen energy, or radioactive materials. Salt rocks, known for their mechanical properties like low porosity, low permeability, high ductility, and exceptional creep and self-healing capacities, were examined using multi-stage triaxial (MSTL) creep data. After resampling, axial strain datasets were recorded at 5--10 second intervals under confining pressure levels ranging from 5 to 35 MPa over 5.8--21 days. Initial analyses, including Seasonal-Trend Decomposition (STL) and Granger causality tests, revealed minimal seasonality and causality between axial strain and temperature data. Further statistical tests, such as the Augmented Dickey-Fuller (ADF) test, confirmed the stationarity of the data with p-values less than 0.05, and wavelet coherence plot (WCP) analysis indicated repeating trends. A suite of deep neural network (DNN) models (Neural Basis Expansion Analysis for Time Series (N-BEATS), Temporal Convolutional Networks (TCN), Recurrent Neural Networks (RNN), and Transformers (TF)) was utilized and compared against statistical baseline models. Predictive performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). Results demonstrated that N-BEATS and TCN models outperformed others across various stress levels, respectively. DNN models, particularly N-BEATS and TCN, showed a 15--20\% improvement in accuracy over traditional analytical models, effectively capturing complex temporal dependencies and patterns.