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Managed aquifer recharge (MAR) is considered an innovative method for storing water in the subsurface. In this work, multi-criteria decision analysis (MCDA) was used to delineate potential groundwater recharge areas for MAR implementation in Western Delta using reclaimed wastewater. By employing geographical information systems (GIS) and pairwise comparison matrix (PCM), a modified approach was utilized for the development of the suitability map by capturing the interlinkages between a specified MAR technique (spreading methods) and MAR suitability mapping processes. The developed approach was created with a range of constraining and factorial considerations. Based on the findings, MAR potential recharge zones included four main suitability classes. The presence of high-suitability areas was mainly delineated in the northeast part, particularly around the left side of the Nile River valley. Areas of low suitability were located around the west-north side where the hydrological criterion seems to hinder the implementation due to the low productivity of the hydrogeologic layer. The developed methodology reflected the importance of specific determining factors (i.e., slope and depth to the water table) that govern the successful implementation of infiltration basins and maximize the benefits from soil aquifer treatments effects when taken into account with other hydrogeological and socio-economic variables. HIGHLIGHTS An enhanced vision for the future management of groundwater in West Delta.; The use of multi-criteria decision analysis for improved groundwater management.; Assessing land suitability for future managed aquifer recharge (MAR) developments.; The use of suitability mapping for better-informed decision-making for MAR planning.;

Climate change and urbanization are likely to increase the vulnerability of cities by increasing the intensity of flooding, which causes damage to the communities. The objective of this research is to estimate the Flood Vulnerability Index (FVI) for Alexandria City to assist decision-makers develop priority urban flood plans and identify methods for reducing flood risk. In this study, the FVI is generally estimated throw three components: social, physical, and environmental. Moreover, eleven major indicators are used to represent each of these three components, and they are classified according to the three flood factors: exposure, susceptibility, and resilience. Geographic information system (GIS), and remote sensing (RS) are applied to obtain the required data and develop maps for identifying the urban flood indictors. The study area has been divided to 16 zones regarding its administrative classification The 16 zones in Alexandria city's Vulnerability Index for the three components (social, physical , and environmental ) and the overall FVI have been estimated. Population, population density, population access to sanitation, and warning system are used as the four indicators for the social component, whereas the four indicators used for the physical components, are approximately of stream, elevation, rainfall amount , and slope. The environmental component, is evaluated by using three indicators , land use, urban growth, and flood recovery time. The index assigns a number among 0 and 1, indicating relatively low or high urban flood vulnerability. The findings demonstrate that FVI offers a way to assess flood vulnerability and obtain appropriate adaptation choices.

Riverbank filtration (RBF) is an affordable technique to provide drinking water with adequate quality. The ultimate objective of this study is to facilitate the transferability and application of this sustainable technique in Egypt. In this work, a numerical model was constructed using Groundwater Modeling System (GMS) to study the effect of four design parameters on the RBF performance parameters (i.e., river filtrate portion and travel time) with the aid of MODPATH and ZONEBUDGET. The design parameters were: (1) the pumping rates of the RBF wells, (2) number of operating wells, (3) distance between wells and the river, and (4) the spacing between wells. This study was focused on the hydraulic aspects of the technique. The results demonstrated that: (1) the river filtrate portion exceeds 75% regardless of the design conditions, and (2) the hydraulic performance of the RBF technique is highly controlled by the production capacity of the wells and their positions relative to the surface water systems; the spacing between wells has a minimum effect. Two equations were developed to estimate the river filtrate portion and minimum travel time as functions of pumping rate and distance between the pumping well and the river. HIGHLIGHTS Hydraulic aspects of riverbank filtration.; Impact of design parameters on river filtrate portion and travel time.; The relation between over-pumping and river filtrate portion.; Multi-regression analysis for RBF performance.;

Background: This research paper studied the parameters affecting the non-revenue water in Egypt. The neural model was developed to forecast the NRW ratio before and after network rehabilitation using main parameters and statistical data for various measured district metered areas (DMAs) in Egypt. The non-revenue water (NRW) ratio is a parameter of significant concern for the water distribution system's performance evaluation. Therefore, NRW ratio parameter evaluation requires analysis of variables that influence the NRW ratio. Aim/objective: This study aims to offer findings that will lay the basis for sustainable water resources management in Egypt.Consequently, a comprehensive evaluation of water distribution systems and parameters affecting NRW was conducted. Methodology: The predictive models were developed using the historical data of the water company for various measured district metered areas (DMAs) in Egypt. The automated meta-models were developed at steady-state and over a prolonged period using a series of principles, rules, and constraints. It was based on multi hidden units of feed-forward neural networks trained by the back-propagation algorithm. Thus, the iterative methods of weight adjustment and activation function of Levenberg-Marquardt was used. Two scenarios were assumed, one that has complete data of the DMA investigated while the other assumed total lack of knowledge. Therefore, four models were built to forecast multiple parameters affecting the percentage of non-revenue water. However, three models were combined to reach the final NRW ratio with no historical data. An optimal number of neurons was determined to be 10. Each model result was tested against numeral statistical indicators showing a different level of accuracy. Results: The model results showed high accuracy in NRW final estimation. The performance indicators (i.e., RMSE, MAE, and correlation) also showed that the machine-learning algorithm better identifies complex relationships between different parameters. The models developed in this research can be applied to other DMAs in Egypt. Overall, these findings indicate that the machine-learning model may be adequate for water companies seeking immediate, cost-effective and long-term improvement of their water distribution systems. Practical implication: Results can provide the basis for decision-makers to reduce costs after applying the rehabilitation process upon any new DMA created. This study presents a modelling approach that provides the best prediction of non-revenue water. It allows the use of a model that enables the accurate projection of NRW ratio and approximating NRW in Egypt and elsewhere in similar countries around the world. Thus, it will help decision-makers enhance the overall NRW ratio strategically across Egypt and elsewhere worldwide.