Steady critical discharge rates from vertical and horizontal wells in fresh–saline aquifers with sharp interfaces

In this study, the critical (or maximum) discharge rates before saline water enters a well were determined for vertical and horizontal wells in a freshwater aquifer which is separated from a static saline aquifer by a sharp interface. Flow around the well was solved by integration of a point sink solution along the well axis, and both the critical discharge rate and critical interface rise were determined through a comparison of the heads and vertical gradients at the saline–fresh water interface. The rates were determined for vertical and horizontal wells with various lengths and depths for different aquifer salinities. Results were generalized by drawing dimensionless type curves. The results showed that the dimensionless total critical discharge rates are higher for the longer horizontal wells and longer vertical wells with a certain bottom depth, and they almost linearly decrease with well depth at rates of 0.7–0.9. For the dimensionless well length of 0.2, the dimensionless total discharge rate of a horizontal well is about 0.1 more than that of a vertical well with the same length and well-top depth. Also, the critical discharge rates per unit length of well are inversely proportional to well length and remarkably higher for shallower wells. Additionally, the critical pumping rate is proportional to the salinity difference of the aquifers. These results were confirmed by comparison to existing solutions for vertical wells with dimensionless lengths of 0.2, 0.5 and 0.6, and for critical interface rises in the range of 0.75–0.9.     

Experimental Evaluation of Construction Waste and Ground Granulated Blast Furnace Slag as Alternative Soil Stabilisers

In this study, the effect of ground granulated blast furnace slag (GGBFS) and recycled construction waste (CW) on bentonite clay stabilisation were investigated. The unconfined compressive strength (UCS) of specimens was evaluated with different combinations of GGBFS and CW over various curing periods. A series of micro analysis tests consisting of scanning electron microscope, energy dispersive spectrometer and X-ray diffraction were also conducted to determine the microstructural arrangement and mineralogical effect of the stabilisation treatment. The UCS results showed an increment in strength after introduction of GGBFS and CW and the longer curing period produced more pronounced results. The optimum additive ratio was calculated as 5 % of slag and 20 % of construction waste under all curing conditions. The micro analytical results also indicated formation of structural bonds between admixtures and bentonite in stabilised specimens, as slag crystals and bentonite particles were observed to occupy the cavities and vesicles on the construction waste grains. However, the experimental data shows that the strength improvement is not significant with the addition of only construction waste.     

Mixed precision parallel preconditioner and linear solver for compositional reservoir simulation

Reservoir simulation role in value creation and strategic management decisions cannot be over emphasized. Simulation of complex challenging reservoirs with millions of grid blocks especially in compositional mode is very time-consuming even with fast modern computers. On the other hand, high price of cluster supercomputers prevents them for being commonly used for fast simulation of such reservoirs. In recent years, the development of many-core processors like cell processors, DSPs, and graphical processing units (GPUs) has provided a very cost-effective hardware platform for fast computational operations. However, programming for such processors is much more difficult than conventional CPUs, and new parallel algorithm design and special parallel implementation methods are needed. Using the computational power of CPUs, GPUs, and/or any other processing unit, Open Computing Language (OpenCL) provides a framework for programming for heterogeneous platforms. In this paper, OpenCL is used to employ the computational power of a GPU to build a preconditioner and solve the linear system arising from compositional formulation of multiphase flow in porous media. The proposed parallel preconditioner is proved to be quite effective, even in heterogeneous porous media. Using data-parallel modules on GPU, the preconditioner/solver runtime reduced at least 1 order of magnitude compared to their serial implementation on CPU.     

Geochemical,isotopic and hydrological mass balance approaches to constrain the lake water–groundwater interaction in Dal Lake,Kashmir Valley

It is important to have qualitative as well as quantitative understanding of the hydraulic exchange between lake and groundwater for effective water resource management. Dal, a famous urban fresh water lake, plays a fundamental role in social, cultural and economic dynamics of the Kashmir Valley. In this paper geochemical, isotopic and hydrological mass balance approaches are used to constrain the lake water–groundwater interaction of Dal Lake and to identify the sources of lake water. Water samples of precipitation (n = 27), lake water (n = 18) and groundwater (n = 32) were collected across the lake and its catchment for the analysis of δ¹⁸O and δ²H. A total of 444 lake water samples and 440 groundwater samples (springs, tube wells and dug wells) were collected for the analysis of Ca²⁺, Mg²⁺, HCO₃ ^?, SO₄ ^2?, Cl^?, NO₃ ^?, Na⁺ and K⁺. Water table and lake water level were monitored at 40 observation locations in the catchment. Water table map including pH and EC values corroborate and verify the gaining nature of the Dal Lake. Stable isotopes of lake water in Boddal and Gagribal basins showed more deviation from the global meteoric water line than Hazratbal and Nigeen basins, indicating the evaporation of lake water. The isotopic and geochemical mass balance suggested that groundwater contributes a significant proportion (23–40%) to Dal Lake. The estimated average groundwater contribution to Dal Lake ranged from 31.2 × 10³ to 674 × 10³ m³ day^?1 with an average of 276 × 10³ m³ day^?1. The study will be useful to delineate the possible sources of nutrients and pollutants entering the lake and for the management of lake water resources for sustainable development.     

Regional and urban population size weights in Saudi Arabia, 1962–1974

The aim of this paper 3s to study the development of population weights for regions and urban centres in Saudi Arabia through the period 1962–1974. In order to achieve this aim some non-parametric statistical rules have been used such as rank-size rule and the four-city index. The results show non-balanced distribution of population on both regional and urban scales. The concentration of people in urban centres is more pronounced than the concentration in regions. This is due to internal and external movement of population towards largesized urban centres. The paper recommends that the official authorities should give more consideration to economic and service development in rural and urban areas that are retreating in ranking and decreasing in population.     

Hydrochemistry and isotopic studies to identify Ganges River and riverbank groundwater interaction, southern Bangladesh

The Ganges River water and riverbank shallow groundwater were studied during a single wet season using the hydrochemical and isotopic composition of its dissolved load. The dissolved concentrations of major ions (Cl^?, SO₄ ^2?, NO₃ ^?, HCO₃ ^?, Ca²⁺, Na⁺, Mg²⁺, and K⁺), trace elements (barium (Ba) and strontium (Sr)) and stable isotopes (O and D) were determined on samples collected from the Ganges River and its riverbank shallow aquifers. In the present study, the shallow groundwater differs significantly from the Ganges River water; it shows distinct high concentrations of Ca²⁺, Mg²⁺, HCO₃ ^?, Ba, and Sr due to water–rock interaction and this in particular suggests that the Ganges River may not contribute significantly to the riverbank shallow aquifers during wet season. Besides, the sum of the total cationic charge (∑⁺, in milliequivalents per liter) in the groundwater shows high values (2.48 to 13.91 meq/L, average 9.12 meq/L), which is much higher than the sum of the cations observed in the Ganges water (1.36 to 3.10 meq/L, average 1.94 meq/L). Finally, the more depleted stable isotopic (δ ¹⁸O and δ ²H) compositions of the Ganges River water are in contrast to those of the riverbank aquifer having enriched stable isotopic values during the wet season and the riverbank groundwater thus has a purely local origin from precipitation.     

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.