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The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

Humidification dehumidification water desalination system using two cycles of air flow (open and closed-air cycles).

In Egyptian desert, growing plants is difficult due to harsh climate (hot at the daytime and cold at the night), infertile soil, low average rainfall and lack of fresh water for irrigation purposes. A set of simple transparent solar stills are integrated with a new solar driven agriculture greenhouse (GH). The stills are placed at the GH roof to use the extra solar radiation (above that required for plant photosynthesis process) for water desalination. In addition to water desalination concept the solar still units even reduce the cooling load during the daytime. A net of aluminum metal coated with black colour is placed on the base of the solar still units to raise the water temperature (enhance desalination process) and provide partially shading for the GH. Using aluminum net decreases also the number of solar still units required to produce the required amount of GH fresh water leading to a significant cost reduction.The main objectives of this work are sizing of the aluminum net, spacing between solar still units to obtain the threshold of plant requirements. Also fresh water production and greenhouse climatic conditions that plant needs (temperature, relative humidity, air velocity and amount of oxygen) are simulated.Numerical simulation was carried out for the hottest day of Borg Elarab, Alexandria (Egypt). 

Land use and climate change always induce significant changes in various parameters of the hydrologic cycle (e.g., surface runoff, infiltration, evapotranspiration). The Wadi El-Assiuti downstream area in the Eastern Desert of Egypt is one of the most promising areas for development that is suffering from insufficient water availability and inadequate water quality for different purposes. The main goal of this research is to evaluate the changes in groundwater quality, land use, and climate in association with geology and flooding during three periods within the years 1997–2019 in the downstream portion of Wadi El-Assiuti in the Eastern Desert of Egypt, using spatiotemporal variation associated with groundwater hydrochemical analysis and GIS techniques. About 133 groundwater samples were collected to examine groundwater quality changes over time. Different groundwater quality indices were calculated, and the results show that TDS levels of groundwater in the study area ranged between 1080–2780 mg/L, 672–4564 mg/L, and 811–6084 mg/L, while SAR levels varied within 6.15–15.34, 1.83–28.87, and 1.43–30.57 for the years 1997, 2007, and 2019, respectively. Both RSBC and SSP values exhibited significantly increasing trends over time. KR values were within 1.36–4.06 in 1997, 0.58–14.09 in 2007, and 0.35–14.92 in 2019; MAR values were within 6.9–45.2 in 1997, 20.79–71.5 in 2007, and 17.71–75.81 in 2019; and PI values were within 60.16–83 in 1997, 45.56–101.03 in 2007, and 42.51–148.88 in 2019. Across the entire study area, ongoing land use changes increased from 1.1% in 1997 to 4.1% in 2019. Findings pointed to the significant contribution of the deep Nubian Sandstone Aquifer to the groundwater aquifer at Wadi El-Assiuti through fractures and deep faults. Given the climatic conditions from 1997–2019, these changes may have affected water quality in shallow aquifers, especially with increasing evaporation. Realizing the spatiotemporal variation of the aquifer recharge system, land use development, and climate change clearly would help in water resource management. This study revealed that flooding events, deep-seated geologic structures, and land use development associated with human activities have the highest impact on groundwater quality.

The main target of this research work is to model the output performance of adsorption water desalination system (AWDS) in terms of switching and cycle time using artificial intelligence. The output performance of the ADC system is expressed by the specific daily water production (SDWP), the coefficient of performance (COP), and specific cooling power (SCP). A robust Adaptive Network-based Fuzzy Inference System (ANFIS) model of SDWP, COP, and SCP was built using the measured data. To demonstrate the superiority of the suggested ANFIS model, the model results were compared with those achieved by Analysis of Variance (ANOVA) based on the maximum coefficient of determination and minimum error between measured and estimated data in addition to the mean square error (MSE). Applying ANOVA, the average coefficient-of-determination values were 0.8872 and 0.8223, respectively, for training and testing. These values are increased to 1.0 and 0.9673, respectively, for training and testing thanks to ANFIS based modeling. In addition, ANFIS modelling decreased the RMSE value of all datasets by 83% compared with ANOVA. In sum, the main findings confirmed the superiority of ANFIS modeling of the output performance of adsorption water desalination system compared with ANOVA.

With the rising population, environmental pollution, and social development, potable water is reducing and being contaminated day by day continually. Thus, several researchers have focused their studies on seas and oceans in order to get potable fresh water by desalination of their saltwater. Solar still of basin type is one of the available technologies to purify water because of free solar energy. The computational fluid dynamic CFD model of the solar still can significantly improve means for optimization of the solar still structure because it reduces the need for conducting large amount of experiments. Therefore, the main purpose of this study is presenting a multi-phase, three-dimensional CFD model, which predicts the performance of the solar still without using any experimental measurements, depending on the CFD solar radiation model. Simulated results are compared with experimental values of water and glass cover temperatures and yield of fresh water in climate conditions of Sheben El-Kom, Egypt (latitude 30.5° N and longitude 31.01° E). The simulation results were found to be in acceptable agreement with the experimental measured data. The results indicated that the daily simulated and experimental accumulated productivities of the single-slope solar still were found to be 1.982 and 1.785 L/m2 at a water depth of 2 cm. In addition, the simulated and experimental daily efficiency were around 16.79% and 15.5%, respectively, for the tested water depth.

This paper investigates the effect of the physical location of the auxiliary source of energy in thermosyphon solar water heaters and shows that the performance of the system can be optimised with respect to the geometry of the system components. The investigation has been based on a domestic thermosyphon solar water heating system, which was simulated using the TRNSYS programme. The annual solar fraction of the system, at the weather and socioeconomic conditions of Cyprus, is, at best, approximately 77% with an in-tank auxiliary heater configuration and 86% with an external auxiliary heater. It is demonstrated that the arrangement with the external auxiliary unit has a higher collector efficiency and results in a higher annual solar fraction. In the case of in-tank auxiliary, the system performance increases with the height of the auxiliary position from the bottom of the storage tank; with the auxiliary at the bottom of the storage tank the annual solar fraction is approximately 59%, compared to 77% when the auxiliary is located at the top of the tank. The system performance also depends on the height of the collector return from the bottom of the tank.

Optimizing the design of water distribution systems often faces difficulties due to continuous variations in water demands, pressure requirements, and disinfectant concentrations. The complexity of this optimization even increases when trying to optimize both the hydraulic and the water quality design models. Most of the previous works in the literature did not investigate the linkage between both models, either by combining them into one general model or by selecting any representative solution to proceed from one model to another. This work introduces an integrated two-step framework to optimize both designs while investigating the reasonable network configuration selection from the hydraulic design view before proceeding to the water quality design. The framework is mainly based on a modified version of the multi-objective particle swarm optimization algorithm. The algorithm’s first step is optimizing the hydraulic design of the network by minimizing the system’s capital cost while maximizing the system’s reliability. The second step targets optimizing the water quality design by minimizing both the total consumed chlorine mass and the accumulated differences between actual and maximum chlorine concentrations for all the network junctions. The framework is applied to Safi Network in Yemen. Three scenarios of the water quality design are proposed based on the selected decision variables. The results show a superior performance of the first scenario, based on optimized 24-h multipliers of a chlorine pattern for a flow-paced booster station, compared to the other scenarios in terms of the diversity of final solutions.