In this study, a series of laboratory experiments were conducted to investigate the energy loss through the hybrid defense system (HDS) in the order of dike, moat, and emergent vegetation in steady subcritical flow conditions. The results of HDS were compared with a single defense system (SDS) comprising only vegetation (OV). The dimensions of dike were kept constant while two different shapes (trapezoidal and rectangular) of moat were considered. The impacts of vegetation of variable thickness and density were investigated. Two combinations of HDS were investigated including the combination of dike and vegetation (DV) and the combination of dike, moat, and vegetation (DMV). The effect of backwater rise due to the vegetation, hydraulic jump formation and the impact of the arrival time of floodwater on energy dissipation were investigated. It was observed that on the upstream side of obstructions, the backwater depth increased by increasing the Froude number in both the SDS and HDS. The hydraulic jump observed in HDS was classified and the energy dissipation due to it was calculated. Under various conditions investigated in this paper, the maximum average energy dissipation was 32% in SDS and 46% in HDS. The trapezoidal moat performed better than rectangular moat as energy dissipater. The delay time was also greater with trapezoidal moat as compared to that in rectangular one. The maximum delay time was 140 s in the case of HDS. Hence, the hybrid defense system offered maximum resistance to the flow of water, thus causing a significant energy loss. For each case of SDS and HDS, empirical equations were developed by regression analysis to estimate the energy dissipation amounts.

Scarcity of water resources is becoming a threatening issue in arid regions like Gulf. Accurate prediction of quantities and quality of groundwater is the first step towards better management of water resources where groundwater is the major source of water supply. Groundwater modelling with respect to its quantity and quality has been performed in this paper using Artificial Neural Networks (ANNs), Adaptive Neurofuzzy Inference System (ANFIS), and hydraulic model MODFLOW. Five types of ANN models with various training functions have been investigated to find the most efficient training function for the prediction of quantity and quality of groundwater, which is an original contribution useful for engineering sector. The results of the hydraulic model, ANFIS, and ANN have been compared. Nash‐Sutcliffe Model Efficiency and Mean Square Error have been used for assessing the performance of models. Taylor’s Diagram has also been used to compare various models. The part of Saq Aquifer lying in the Qassim Region has been investigated as the study area. Modern tools, including Geographical Information System (GIS) and Digital Elevation Model (DEM) are applied to process the required data for modelling. Climatic, geographical, and quality of groundwater (contaminants) data are obtained from the Ministry of Environment, Water, and Agriculture, Jeddah/Riyadh. ANFIS model is found to be the most efficient for modelling both the quality and quantity of the aquifer. Sensitivity analysis was performed, and then various future scenarios were investigated for sustainable groundwater pumping. The results of the research will be useful for the community and experts working in the field of water resources engineering, planning, and management in arid regions.

Artificial Intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and dimensional analysis-based empirical equations (DAEEs), can estimate scour depth around bridge piers. AI’s accuracy depends on various architectures, while DAEEs’ performance depends on experimental data. This study evaluated the performance of AI and DAEEs for scour depth estimation using flow velocity, depth, size of bed sediment, critical approach velocity, and pier width. The data from a smooth rectangular (20 m × 1 m) flume and a high-precision particle image velocimetry to study the flow structure around the pier - width: 1.5 – 91.5 cm evaluated DAEEs. Various ANNs (5, 10, and 15 neurons), double layer (DL) and triple layers (TL), and different ANFIS settings were trained, tested, and verified. The Generalized Reduced Gradient optimization identified the parameters of DAEEs, and Nash–Sutcliffe efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of different models. The study revealed that DL ANN-3 with 10 neurons (NSE = 0.986) outperformed ANFIS, other ANN (ANN1, ANN2, ANN4 & ANN5) models, and empirical equations with NSE values between 0.76 and 0.983. The study found pier dimensions to be the most influential parameter for pier scour.

Abutment scour is a major cause of bridge failures worldwide, leading to disruptions, economic losses, and loss of life. The present experimental study examines countermeasures against abutment scour using hooked-collar protections on vertical-wall and wing-wall abutments (at 45° and 60°) under different flow conditions. All 60 experiments were performed under sub-critical flow conditions by investigating scour around an abutment 20 cm long, 20 cm wide, and 25 cm tall. Two distinct values of the Froude number, 0.154 and 0.179, and a sediment particle diameter (d₅₀) of 0.88 mm were used throughout the experimental phase. The resulting equilibrium scour around the abutments was compared to those with collar and hooked-collar protections. It was determined that the maximum abutment scour depth reduction was 83.89% when hooked collars were placed on vertical wall abutments beneath the bed surface level, and for wing-wall abutments at 45° and 60°, it was 74.2% and 73.5%, respectively, at the bed surface level. Regression analysis was conducted to assess the non-dimensional scour depth (D_s/Y_f) and scour reduction (R_Ds/Y_f), with a high enough coefficient of determination (R² values of 0.96 and 0.93, respectively), indicating high confidence in the analysis. The sensitivity analysis findings demonstrate that the width of the collar (W_c) and L_a are the most influencing factors affecting D_s/Y_f and RD_s/Y_f.

Water scarcity is a major issue for developing countries due to the continuous increase in population every year, the major environmental challenges faced by developing countries such as Pakistan being the scarcity of water. One proposed solution to meet the requirements is to conserve water from rainfall. The process consists of the collection, storage, and use of rainwater. The rooftop rainwater harvesting systems (RWH) and rainfall harvesting system for artificially recharged water by recharge wells have received increased attention in the recent past as an efficient means of water conservation. In this study, both the systems have been analyzed for the University of Engineering and Technology Taxila (UET Taxila), Pakistan. The objective of this study is to propose a system to harvest water from the rooftops of all of the buildings on the campus and also to propose the most optimum locations of recharge wells for the artificial recharge of groundwater development. Numerous field visits were conducted after every rainfall over the past few months to identify lower elevation areas, which were further validated by the results obtained by Arc GIS. The total area of catchments available for rainwater harvesting in UET Taxila and the amount of water that could be harvested or used for replenishing groundwater reserves were also assessed in the current study. The results show that the harvestable rooftop water per month is 59% of the currently available source for watering trees and plants, and the harvestable water by recharge wells is 761,400 ft³ per year.