In the present study, the flow structure of discontinuous double-layered vegetation patches was investigated using a 3D Reynolds stress turbulence model (RSM). The channel domain was built using GAMBIT (Geometry and Mesh Building Intelligent Toolkit). For the simulation and postprocessing, FLUENT (ANSYS) was used to analyze the distribution of the mean velocity, Reynolds stresses, and other flow properties against two different flow conditions. The results captured by the turbulence model at specific locations and the cross section are presented in the form of various velocity profiles and contour plots. In the gap portion, the velocity was visibly lower than that in the vegetation areas, while the influence of patch distribution was not visible in the overlying flow layer. The velocity profiles at critical locations were categorized by numerous modulation points and velocity projections close to the bed, principally for positions straight after the vegetation structures. A distinction in the velocity at the topmost of the smaller vegetation structure was prominent. Reynolds stresses, turbulent kinetic energy, and turbulence intensity exhibited large fluctuations inside the vegetation regions and just behind the vegetation structures compared with in the gap regions.

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

In this research, an assessment of scour depth prediction in meandering channels with spur dikes is made employing machine learning approaches. Efficient determination of the scour depth is therefore vital in the prediction of morphologic aspects and structural stability. The input parameters include sinuosity (S), spur dike locations (Ld), and porosity (P) with experimental data from sinusoidal flumes. Four machine learning models; Extreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) XGBoost-PSO, Random Forest (RF), k-Nearest Neighbors (k-NN), and Decision Tree-Neural Network (DT-NN) were used and compared. The findings demonstrate an R-value of 0.995 in the case of RF model while XGBoost-PSO gave second-best accuracy with R = 0.988. The results of the SHAP analysis illustrated that porosity and sinuosity are significant factors affecting scour depth (Ds/Yn, Ds: scour depth, Yn: water depth) and had moderate importance assigned to spur dike location. Kernel density plots further supported the RF model regarding error distribution consistency. Even though, both XGBoost-PSO yielded better results because of hyperparameter tuning, k-NN and DT-NN had less precise outcomes specifically predicted for progressive hydraulic procedures. Taylor's diagram even revealed greater accuracy of prediction by RF. Hence, a proper selection of appropriate machine learning models remains the first step in estimating scour depth sufficiently for flood and erosion control.

This study investigates the role of emerged vegetation in enhancing the performance of submerged spur dikes for better flow control and bank protection in river systems. The research utilizes a numerical model based on Computational Fluid Dynamics (CFD), validated with experimental data. After validation, the study explores various configurations of vegetated spur dike, adjusting the submergence heights of the impermeable spur dike to achieve porosity ranging from fully impermeable to highly porous. Porosity levels of 24%, 48%, and 72% were chosen based on the spur dike height and the effectiveness of staggered vegetation arrangements in maximizing drag and reducing velocity. This approach aims to determine their impact on flow structure, velocity, and turbulence characteristics. The results reveal that impermeable dikes create strong recirculation zones downstream, increasing the potential for bank erosion. Introducing vegetation on the dike, particularly at moderate porosities (24% and 48%), effectively disrupts this behavior by reducing downstream velocity and mitigating mass and momentum exchange concentration between the spur dike field and the mainstream. However, the highest porosity case (72%) offered reduced flow resistance, limiting its protective effectiveness. Analysis of velocity and stress distributions showed that vegetation porosity significantly impacts normal and shear stresses, influencing flow stability at critical points around the spur dike. The findings signify the potential of integrating vegetation into spur dike designs to achieve a balance between effective flow conveyance and erosion control even in the spur dike submergence condition. This approach can outperform conventional emerged impermeable spur dikes, as supported by previous studies that demonstrate the effectiveness of porous and vegetated structures in reducing flow resistance, minimizing stagnation zones, and improving sediment deposition compared to impermeable designs.