Groundwater is an essential resource for drinking and agricultural purposes in the Al-Jouf region, Saudi Arabia. The main objective of this study is to assess groundwater quality for drinking and irrigation purposes in the Al-Jouf region. Physicochemical characteristics of groundwater were determined, including total dissolved solids (TDS), pH, electric conductivity (EC), hardness, and various anions and cations. The groundwater quality index (WQI) was calculated to determine the suitability of groundwater for drinking purposes. The EC, sodium percentage (Na⁺ %), magnesium hazard (MH), sodium adsorption ratio (SAR), potential salinity (PS), and Kelley’s ratio (KR) were assessed to evaluate the suitability of groundwater for irrigation. Effective statistical tests and Feed-forward neural network (FFNN) modeling were applied to reveal the correlation between parameters and predict WQI. The results indicated that approximately all samples are appropriate for drinking and irrigation uses except samples of the Al Qaryat region. The ionic abundance ranking was Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ for cations, and Cl⁻ > SO₄²⁻ > NO₃⁻ for anions. Moreover, the groundwater is dominated by alkali metals (K⁺ and Na⁺) and controlled by the rock–water interaction process. The indicators of groundwater quality for irrigation and drinking according to the following criteria (Na⁺ %, SAR, KR, MH, PS, WQI (WHO), and WQI (BIS)) can be predicted by the FFNN with root mean square errors (RMSE) of 0.136, 0.070, 0.022, 0.073, 2.45 × 10⁻³, 1.45 × 10⁻², and 1.18 × 10⁻², respectively, and R² of 0.99, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00, respectively.

Adsorption is an effective and economical alternative to remove herbicides from polluted water. The aim of this study is to investigate the adsorption of the most common herbicides (2,4-dichlorophenoxy-acetic acid (2,4-D) and 4-chloro-2-methylphenoxyacetic acid (MCPA)) onto activated carbon (AC) fabricated from wheat straw under different conditions. The adsorption of MCPA and 2,4-D onto the selected AC (CLW) and the effects of the ionic strength, the solution pH, and the presence of microorganisms in the medium were investigated. The results showed that the selected AC had a high surface area (1437 m²/g). The adsorption rate increased with an increase in the AC mass. The selected AC had a higher adsorption capacity (1.32 mmol/g) for 2,4-D compared to MCPA (0.76 mmol/g). The adsorption of 2,4-D and MCPA was not affected by variation in the solution pH. However, the presence of electrolytes exerted a major effect on adsorption. The presence of microorganisms enhanced adsorption onto the AC by 17% and 32% for 2,4-D and MCPA, respectively. Moreover, a radial basis function neural network (RBFNN) was employed to accurately predict the adsorption capacity based on the pollutant type, carbon dose, initial concentration, pH, ionic strength, and presence of bacteria. The RBFNN showed excellent accuracy in predicting the adsorption capacity, with an R² value of 0.96 and a root mean square error (RMSE) of 0.054. These findings showed that the AC fabricated from biomass residues of wheat straw is a promising option to recycle this type of biomass waste and reduce environmental threats, consequently contributing to achieving sustainability.

Cutoff walls are widely used to limit seepage, piping, and the uplift under hydraulic structures. Therefore, this study focused on a numerical investigation of the hydraulic performance of cutoff walls beneath hydraulic structures during both static and dynamic conditions, considering location and inclination angle influences. The results confirmed that placing the cutoff wall at the upstream heel was more effective in reducing uplift pressure compared to other placements during static conditions. The inclination angles for the different placements of the cutoff wall had a significant impact on the total uplift pressure, exit hydraulic gradient, and seepage discharge during both static and dynamic states. The earthquakes had a noticeable effect on uplift pressure, seepage discharge, and exit hydraulic gradient. During static conditions, the inclination angle of 90° was the most effective angle for decreasing seepage discharge, irrespective of the cutoff wall position. During an earthquake, the seepage discharge values were high regardless of the inclination angle. In the case of placing a cutoff wall at the upstream heel, the maximum seepage discharge value occurred at an inclination angle of 45°. This study provided insights into the behavior of cutoff walls under different conditions and can inform the design and construction of such structures for effective seepage control. The experimental feed-forward neural network (FFNN) was also successfully built. According to the following criteria (uplift pressure, seepage, and exit hydraulic gradient), the hydraulic performance of cutoff walls beneath hydraulic structures under static conditions can be examined. The FFNN can make predictions with root mean square errors (RMSE) of 0.0697, 0.0021, and 0.0059, respectively, and R² values of 1.00, 0.9994, and 0.9997.

Heavy metals, extensively used in various industrial applications, are among the most significant environmental pollutants due to their hazardous effects on human health and other living organisms. Removing these pollutants from the environment is essential. In this study, activated carbon (AC) (Carbon C) was employed to eliminate Pb(II) from water. The optimal removal conditions were determined as follows: a 50 mg dose of activated carbon, an initial Pb(II) concentration of 100 mg/L, pH 4, a temperature of 30 °C, and a contact time of 60 min Under these conditions, activated carbon achieved a Pb(II) removal efficiency of approximately 97.86%. The adsorption data for Pb(II) closely aligned with the 2nd-order kinetic model, and the equilibrium data were effectively described by the Langmuir isotherm equation. The maximum adsorption capacity of Pb(II), as determined by the Langmuir model, was 48.75 mg/g. These methods were successfully applied to remove Pb(II) from various environmental and industrial wastewater samples. To accurately predict the percentage of Pb(II) removal based on parameters such as pollutant type, carbon dosage, pH, initial concentration, temperature, and treatment duration, feed-forward neural networks (FFNNs) were utilized. The FFNN model demonstrated outstanding predictive accuracy, achieving a root mean square error (RMSE) of 0.03 and an R² value of 0.996.

This study investigates the combined effect of wheat straw particle size and mixing ratio on the anaerobic co-digestion (ACD) of waste activated sludge under mesophilic conditions. Ten batch digesters were tested with varying straw-to-sludge ratios (0–1.5%) and particle sizes (5 cm, 1 cm, and <2 mm). Fine straw particles (<2 mm) at 1.5% loading achieved the highest removal efficiencies for TS (43.55%), TVS (47.83%), and COD (51.52%), resulting in a 140% increase in biogas yield and methane content of 60.15%. The energy recovery reached 14.37 kWh/kg, almost double the control. The developed Recurrent Neural Network (RNN) model (3 layers, 13 neurons, 500 epochs) predicted biogas production with 99.8% accuracy, a root mean square error (RMSE) of 0.0038, a mean absolute error (MAE) of 0.0093, and an R² close to 1. These results confirm the potential of integrating agricultural residues into wastewater treatment for renewable energy recovery and emission reduction. This study uniquely integrates mechanical pretreatment of wheat straw with RNN-based modeling to enhance biogas generation and predictive accuracy. It establishes a dual-experimental AI framework for optimizing sludge–straw co-digestion systems. This approach provides a scalable, data-driven solution for sustainable waste-to-energy applications.

Understanding and accurately predicting the outflow hydrograph from embankment dam breaches is essential for managing the associated flood hazard and improving emergency preparedness. This work simulates the breaching process using a high-resolution 3D computational fluid dynamics (CFD) model, a critical natural hazard for earth-fill dams under overtopping conditions. The model was validated against the experimental data, showing high accuracy in predicting breach development and failure timing. A parametric analysis was performed to assess the influence of the initial breach geometry on erosion dynamics. The results indicated a high sensitivity, while increasing the breach width by 5% led to an average 11% increase in the erosion rate, and decreasing the depth by 5% caused an average 16.5% rise. To enhance predictive capabilities for this hazard, a multilayer neural network (MLNN) was trained on the CFD-generated dataset. The network utilized breach geometry and time as inputs to forecast the peak outflow and erosion rate, achieving excellent accuracy (RMSE = 0.019, R² = 0.99). This integrated modeling strategy combines data-driven learning with physics-based simulation and demonstrates its effectiveness for laboratory-scale dam breach modeling. This approach is a step toward more efficient surrogate-based tools for flood risk analysis, though its extension to full-scale dams and varied material properties requires additional validation and scaling analyses beyond the scope of this work.