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River discharge estimation is vital for effective flood management and infrastructure planning. River systems consist of a main channel and floodplains, collectively forming a compound channel, posing challenges in discharge calculation, particularly when floodplains converge or diverge. In the present study, ML algorithms such as XGBoost, CatBoost, and LightGBM were developed to predict discharge in a compound channel. PSO algorithm is applied for optimization of hyperparameters of gradient boosting models, denoted as PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost. ML model discharge predictions were validated with existing empirical models and feature importance was explored using SHAP and sensitivity analysis. Results show that all three gradient-boosting algorithms effectively predict discharge in compound channels and are further enhanced by application of PSO algorithm. The R2 values for XGBoost, PSO-XGBoost, CatBoost, and PSO-CatBoost exceed 0.95, whereas they are above 0.85 for LightBoost and PSO-LightBoost. PSO-CatBoost performance is better than other models based on findings of statistical performance parameters, uncertainty analysis, reliability index, and resilience index for prediction of discharge in a compound channel with converging and diverging flood plains. The findings of this study validate the suitability of the proposed models especially optimized with PSO is recommended for predicting discharge in a compound channel.

The failure of bridges, attributed to bridge pier scouring, poses a significant challenge in ensuring safe and cost-effective design. Numerous laboratory and field experiments have been conducted to comprehend the mechanisms and predict the maximum equilibrium scour depth around bridge piers. Over the last eight decades, various empirical methods have been developed, with different authors incorporating diverse influencing parameters that significantly impact the estimation of equilibrium scour depth around bridge piers. This paper aims to consolidate: (1) available experimental and field data sets on different types of bridge pier scouring, (2) the influence of flow and roughness parameters on both clear water scouring (CWS) and live bed scouring (LBS), and (3) existing empirical equations suitable for computing equilibrium scour depth around a bridge pier under CWS and LBS conditions. The presented research encompasses over 80 experimental/field data sets and more than 60 scour-predicting equations developed for CWS and LBS conditions in the past eight decades. Based on the performance of different empirical models in predicting scour depth ratio, suitable models are recommended for CWS and LBS conditions.

Scour depth prediction is an important aspect of designing a bridge pier structure in a river. Proper modeling of scour depth ensures the sustainability of the structure. An attempt is made to develop a scour depth model for the bridge pier using an adaptive network-based fuzzy inference system (ANFIS) and gene expression programming (GEP). The scour depth is found to be influenced by various independent parameters such as pier diameter, flow depth, approach mean velocity, critical velocity, Froude number, bed sediment, and geometric standard deviation of bed particle size. Gamma tests are performed to identify the best input parameter combinations to predict scour depth. In the present study, two separate models have been developed for clear-water scouring (CWS) and live-bed scouring (LBS). For different ranges of input parameters, the scour depth ratio is computed and error analysis is performed. Results indicate that the ANFIS model (R2CWS = 0.95, MAPECWS = 9.39% and R2LBS = 0.95, MAPELBS = 5.29%) is the most accurate predictive model in both scour conditions as compared to the GEP model and existing models of previous researchers. However, for the low value of pier diameter (b) to flow depth (y) ratio (<0.25), the present ANFIS model apportioned unsatisfactory results for LBS only.

The Flow Separation Zone (FSZ) is a key hydrodynamic feature of open channel confluences, and its reduction contributes to improved channel efficiency. This study employs the open-source computational fluid dynamics (CFD) software OpenFOAM to examine the influence of a bed sill on reducing the FSZ at a right-angled open channel confluence. The flow is simulated by solving the three-dimensional (3D) Reynolds-averaged Navier–Stokes (RANS) equations with the SST k–ω turbulence model, while the interFoam multiphase solver is used to capture the water–air interface via the Volume of Fluid (VOF) method. The numerical model is validated against experimental data from the literature by comparing velocity fields and water surface elevations. Different sill sizes, locations, and configurations are tested to evaluate their effectiveness in reducing the FSZ. The results indicate that strategically placed sills can shorten the FSZ length by up to 55%, while modifying velocity profiles and reducing flow recirculation. Using multiple sills, particularly a three-sill configuration, further enhances this effect, reducing the FSZ length by as much as 74%. Analysis of secondary currents shows that sills intensify these currents, thereby enhancing momentum exchange and reducing the flow separation. Furthermore, energy loss analyses conducted for various sill configurations demonstrate that strategically positioned sills can reduce the energy loss significantly.

Flood risk assessment (FRA) is a process of evaluating potential flood damage by considering vulnerability of exposed elements and consequences of flood events through risk analysis which recommends the mitigation measures to reduce the impact of floods. This flood risk analysis is a technique used to identify and rank the level of flood risk through modeling and spatial analysis. In the present study, Musi River in the Osmansagar basin is taken in to consideration to evaluate the flood risk, which is located at Hyderabad. The input data collected for the study encompasses Hydrological and Meteorological datasets from Gandipet Guage station in Hyderabad, raster grid data for Osmansagar basin along with several indicators data influencing flood vulnerability. The primary research objective is to conduct a quantitative assessment of the Flood vulnerability index (FVI), to develop a comprehensive flood risk map and to evaluate the magnitude of damaging flood parameters, inundated volume and to analyze the regions inundated in the study area. In risk analysis, FVI determines the degree of which an area is susceptible to the negative impact of flood through various influencing indicators, Flood hazard map segregate the regions based on flood risk level through spatial analysis in Arc-GIS. A part of this study includes an integrated methodology for assessing flood inundation using Quantum Geographic Information Systems (QGIS) data modelling for spatial analysis, Hydraulic Engineering Center’s River Analysis System (HEC-RAS) hydraulic modelling for unsteady flow analysis and a machine learning technique i.e. XGBoost, to enhance the accuracy and efficiency of flood risk assessment. Subsequently, inundation map produced using HEC-RAS is superimposed with building footprints to identify vulnerable structures. The results obtained by risk analysis using hydraulic modeling, GIS analysis, and machine learning technique illustrates the flood vulnerability, areas having high flood risk and inundated volume along with predicted flood levels for next 10 years. These findings demonstrate the efficiency of the holistic approach in identifying vulnerability, flood-prone areas and evaluating potential impacts on infrastructure and communities. The outcomes of the study assist the decision-makers to gain valuable insights into flood risk management strategies.

Scouring around a bridge pier involves removing sediment from the riverbed and banks due to water flow. This paper employs eXtreme Gradient Boosting (XGBoost) and support vector machine with particle swarm optimization (SVM-PSO) machine learning (ML) approaches to model the temporal local scour depth around bridge piers under clear water scouring (CWS) conditions. CWS datasets, incorporating bridge pier geometry, flow characteristics, and sediment properties, are collected from existing literature. Five non-dimensional influencing parameters, such as ratio of pier width to flow depth (b/y), ratio of approach mean velocity to critical velocity (V/Vc), ratio of mean sediment size to pier width (d50/b), Froude number (Fr), and standard deviation of sediment (σg), are chosen as input parameters. XGBoost and SVM-PSO ML models demonstrate superior predictive capabilities, achieving coefficient of determination (R2) values exceeding 0.90 and mean absolute percentage error (MAPE) and root mean square error (RMSE) values less than 17.07% and 0.0341, respectively. Comparison with the previous four empirical models based on statistical indices reveals that the proposed XGBoost model outperforms SVM-PSO and empirical models in predicting scour depth, so it is recommended for estimating clear water scour depth under varying temporal conditions within the specified dataset range.

The current study investigated a detailed account of phytocompounds within Coccinia grandis using GC-MS coupled with high-performance liquid chromatography. An in-silico approach was employed to gain insight into the inhibitory mechanism of Lupeol on Klebsiella pneumoniae. The molecular dynamics simulations were conducted over 100 ns to investigate the stability of metallo-β-lactamase with Lupeol, Imipenem (IPM), and Meropenem (MRP). We meticulously explored the antimicrobial activities of the crude extract of C. grandis leaves (CGL) and Lupeol against carbapenem-resistant K. pneumoniae . Additionally, cellular disruption activity was verified using a scanning electron microscope to better understand the antimicrobial activity of Lupeol. The computed free binding energy evaluated for Lupeol was − 92.380 ± 2.261 kJ/mol. This substantial negative value suggested the robust inhibitory potential of Lupeol, indicating a strong and stable interaction between Lupeol and target protein gold-bound NDM-1. Furthermore, the in-vitro antimicrobial activity of CGL and Lupeol were compared with standard antibiotics; MRP and IPM through the disc diffusion method. The zone of inhibition, and minimum inhibitory concentration, were found to be 23 ± 0.57 and 0.02 ± 0.01 mg/ml for Lupeol, 14.33 ± 0.58 mm and 0.03 ± 0.01 mg/ml for CGL. The ZOI of 12.67 ± 0.58 mm for IPM and 12.33 ± 0.58 mm for MRP was observed whereas 0.13 ± 0.06 mg/ml and 0.17 ± 0.06 mg/ml of MIC was determined for IPM and MRP respectively. Furthermore, the mechanism of action of Lupeol demonstrated significant activity in cell wall disruption assay and NDM-1 enzyme inhibition assay. Moreover, Lupeol also showed apoptotic activity against various cancer cell lines and no cytotoxic effect against healthy cell line. This study suggested Lupeol as an alternative natural therapeutic compound as an inhibitor of β-lactam resistant K . pneumoniae .

Experiments have been conducted in three diverging compound channels for different flow conditions to study the flow distribution in floodplain, upper and lower main channel. In a compound channel, vertical apparent shear exists on the interface between the upper main channel and the floodplain, which generally accelerates the flow on the floodplain and resists the flow in the upper main channel. In addition, a horizontal apparent shear stress also occurs on the interface between the upper and lower main channels, which generally accelerates the flow in the lower one and resists the flow in the upper one. Therefore, it is essential to consider the exchanges of momentum at both vertical and horizontal shear layer regions. In this paper, an attempt is made to improve the classical independent subsection method (ISM) to determine the magnitudes of flow and velocities in both upper and lower main channels. Four subsections are created in improved ISM according to the vertical and horizontal division lines that correspond to the vertical interface between the main channel and floodplain and the horizontal interface between upper and lower main channels respectively. The improved ISM consists in a set of four coupled 1D momentum equations (instead three equations of classical ISM) for subsections and a mass conservation equation for the total cross-section. The computed results show that the method is well capable of predicting the discharge distributions in the floodplain and main channel (both at upper and lower main channel).

The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage systems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R2 value of more than 0.90.