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.
The efficiency of flow energy reduction past emergent vegetation has been typically studied assuming a right angle of the vegetated corridor to the flow direction. However, in many real-world cases the riparian zones of natural, restored, or engineered rivers and waterways, are found at an oblique angle to the flood flow direction. In the current study, the effect of vegetation angle with respect to the flow direction is investigated experimentally in an open channel rectangular flume. The experiments are conducted under a range of subcritical steady flow conditions, with varying Froude number (Fr o ). The vegetation cover is placed at various angles to the flow direction (90 , 45 , and 30 ), for a sparse and intermediate vegetation density, defined from the ratio of spacing of each vegetation element in the cross stream direction (B), and the diameter of vegetation element (d) (B/d = 2.13 and 1.09, respectively). Detailed water surface profiles are obtained for all those cases, demonstrating a considerable backwater rise, increasing with increasing vegetation density, Froude number, and flow approach angle. The energy reduction decreased by increasing the Froude number for the perpendicular (90 ) and increased for oblique vegetation (45 and 30 ). For the perpendicular vegetation, the average energy reduction rate for sparse (90VS) and intermediate (90VI) vegetation densities are 25.44% and 31.44%, respectively. The range of average energy reduction for sparse vegetation at 45 (45VS) and at 30 (30VS) are 18.3-19.8% and 18.7-19.7%, respectively. Similarly, range of average energy reduction for intermediate vegetation at 45 (45VI) and at 30 (30VI) are 21.4-22% and 18.8-22%, respectively.
Water resources are directly related to the economic conditions of a region. Precise estimation of groundwater is an important step toward better planning and management. This book chapter is dedicated to modelling groundwater in terms of both quantity and quality utilizing ANN (artificial neural networks), ANFIS (adaptive neuro-fuzzy inference system), and the numerical-hydraulic modeling by MODFLOW (modular three-dimensional finite-difference groundwater flow model). The model performance was determined using mean of square error and Nash-Sutcliffe efficiency of model. The pumping data of the area was used to determine the parameters of Saq Aquifer (Qassim, Saudi Arabia) including specific storage and transmissivity. It has been found that the ANFIS model is the most effective for qualitative and quantitative modelling of the aquifer. Following sensitivity analysis, different future scenarios for sustainable groundwater pumping were examined. This book chapter presents research findings that will be useful for engineers, planners, and managers of water systems in arid areas.
The present study investigates the effect of upstream structure on the bulk drag coefficient of vegetation experimentally by placing an embankment model with or without moat/depression upstream of the vegetation. The results indicate that in the presence of the upstream structure, the bulk drag coefficient of vegetation is decreased because the upstream structure shares the drag with vegetation. Further, it is noticed that by placing only the embankment on the upstream side, the maximum decrease in the bulk drag coefficient is 11%, and by placing both embankment and moat models on the upstream side of the vegetation, a 20% decrease in the bulk drag coefficient is observed. Based on the variables affecting the bulk drag coefficient, regression models and Artificial Neural Network (ANN) models are developed to predict the bulk drag coefficient. The results from five ANN models with different training functions are compared to find the best possible training function. The coefficient of determination (R2), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Sum of Square Error (SSE), Mean absolute error (MAE), and Taylor's diagrams are used to evaluate the performance of ANN modeling techniques. The ANN model having nine neurons in each hidden layer, performs best among the five models, as this model shows the optimal values for the performance indicators, such as the highest R2 and NSE, and minimum values for the RMSE, SSE, and MAE. Finally, the comparison between the regression model and the ANN model shows that the best ANN model, achieving R2 values of 0.99 and 0.98 for the training and validation subsets, outperforms the regression models.
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