In arid and semi-arid lands like Iran water is scarce, and not all the wastewater can be treated. Hence, groundwater remains the primary and the principal source of water supply for human consumption. Therefore, this study attempted to spatially assess the groundwater potential in an aquifer in a semi-arid region of Iran using geographic information systems (GIS)-based statistical modeling. To this end, 75 agricultural wells across the Marvdasht Plain were sampled, and the water samples’ electrical conductivity (EC) was measured. To model the groundwater quality, multiple linear regression (MLR) and principal component regression (PCR) coupled with elven environmental parameters (soil-topographical parameters) were employed. The results showed that that soil EC (SEC) with Beta = 0.78 was selected as the most influential factor affecting groundwater EC (GEC). CaCO₃ of soil samples and length-steepness (LS factor) were the second and third effective parameters. SEC with r = 0.89 and CaCO₃ with r = 0.79 and LS factor with r = 0.69 were also characterized for PC1. According to performance criteria, the MLR model with R² = 0.94, root mean square error (RMSE) = 450 µScm⁻¹ and mean error (ME) = 125 µScm⁻¹ provided better results in predicting the GEC. The GEC map indicated that 16% of the Marvdasht groundwater was not suitable for agriculture. It was concluded that GIS, combined with statistical methods, could predict groundwater quality in the semi-arid regions.

Due to the physical processes of floods, the use of data-driven machine learning (ML) models is a cost-efficient approach to flood modeling. The innovation of the current study revolves around the development of tree-based ML models, including Rotation Forest (ROF), Alternating Decision Tree (ADTree), and Random Forest (RF) via binary particle swarm optimization (BPSO), to estimate flood susceptibility in the Maneh and Samalqan watershed, Iran. Therefore, to implement the models, 370 flood-prone locations in the case study were identified (2016–2019). In addition, 20 hydrogeological, topographical, geological, and environmental criteria affecting flood occurrence in the study area were extracted to predict flood susceptibility. The area under the curve (AUC) and a variety of other statistical indicators were used to evaluate the performances of the models. The results showed that the RF-BPSO (AUC=0.935) has the highest accuracy compared to ROF-BPSO (AUC=0.904), and ADTree-BPSO (AUC=0.923). In addition, the findings illustrated that the chance of flooding in the center of the area in question is greater than in other points due to lower elevation, lower slope, and proximity to rivers. Therefore, the ensemble framework proposed here can also be used to predict flood susceptibility maps in other regions with similar geo-environmental characteristics for flood management and prevention.

Gates and weirs are frequently used hydraulic structures employed for controlling water flow rates in irrigation and drainage networks. Therefore, accurately estimating the discharge coefficient (Cd) is important for precise flow measurement. The present study used intelligent predictive models for modeling Cd in labyrinth sluice gates. For this purpose, key dimensionless parameters and reliable experimental datasets were used. The support vector regression (SVR) model was hybridized with particle swarm optimization (PSO) and genetic algorithms (GA). The statistical metrics and graphical plots evaluated the performance of the generated models. Three commonly used statistical indicators, namely root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), were used for quantitatively evaluating the performance of the proposed models. The SVR-PSO model achieved the lowest values of RMSE (0.0287) and MAE (0.0209) and the highest value of R2 (0.9732), indicating that it was more accurate than SVR-GA (RMSE = 0.0324, MAE = 0.0257, R2 = 0.9685) and SVR (RMSE = 0.0575, MAE = 0.0468, R2 = 0.8958) on the testing data. The findings revealed that the hybrid SVR methods were more accurate than the standalone SVR model. In addition, regarding the value of the objective function criterion (OBF), the SVR-PSO (OBF = 0.0245) and SVR-GA (OBF = 0.0273) had lower OBF values and provided more precise estimates of the Cd compared to existing nonlinear regression-based formulas and existing data-driven approaches. Finally, sensitivity and SHapley Additive exPlanations (SHAP) analyses determined the relative importance of each input variable for the prediction of Cd.

Channel confluences happen commonly in water transport networks and natural rivers. Utilizing a 3D CFD code, a series of numerical simulations were performed using a large eddy simulation turbulence model to investigate the effect of the variations in tributary channel width and the transverse geometrical shape of the main channel on the flow parameters and vertical structure in a T-shape confluence. The code was calibrated using the experimental data from the literature. Flow parameters were considered in ratios of tributary width to the main channel width in trapezoidal and rectangular channels. Results indicate that decreasing the width ratio of the tributary channel to the main channel significantly affects the flow structure in the confluence. Generally, it increases the width and length of the main recirculation zone. It also increases the maximum velocity near the bed, especially in cases with a trapezoidal shape. Besides, it highly affects the structure and formation of the recirculation zone in trapezoidal channels.

Channel confluences are of the common structures in fluid transport channels. In this study, a series of numerical simulations were performed, utilizing a 3D code to investigate the reaction of the flow parameters and vortical structure to the variations in flow discharge and its Froude number from both main channel and tributary branch in a T-shape junction. The code was calibrated with the experimental data. Parameters, including the velocity, the turbulence energy, stream surface profile, head losses, and the transverse flow motions, were considered in different situations. It was concluded that increasing the ratio of discharge of flow from side-channel to the main channel (Q*) increased the area and power of the recirculation zone, as well as the width of separation plate downstream of the confluence, while it reduced the area of the stagnation zone (or the wake vortex) within the side-channel. It was also indicated that increasing the discharge ratio from side-channel resulted in an increase in the upstream water level in the main channels, which was dependent on the upstream discharge.

The journal retracts the article titled “Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique”