Network Capacity Assessment and Increase in Systems with Intermittent Water Supply

Assessing Water Security in Water-Scarce Cities: Applying the Integrated Urban Water Security Index (IUWSI) in Madaba, Jordan

Water security is a major concern for water-scarce cities that face dynamic water challenges due to limited water supply, climate change and increasing water demand. Framing urban water security is challenging due to the complexity and uncertainties of the definitions and assessment frameworks concerning urban water security. Several studies have assessed water security by granting priority indicators equal weight without considering or adapting to the local conditions. This study develops a new urban water security assessment framework with application to the water-scarce city of Madaba, Jordan. The study applies the new assessment framework on the study area and measures urban water security using the integrated urban water security index (IUWSI) and the analytic hierarchy process (AHP) as a decision management tool to prioritise and distinguish indicators that affect the four dimensions of urban water security: drinking water, ecosystems, climate change and water-related hazards, and socioeconomic aspects (DECS). The integrated urban water security index (IUWSI) highlights the state of water security and intervention strategies in Madaba. The study reveals that urban water security in Madaba is satisfactory to meet basic needs, with shortcomings in some aspects of the DECS. However, Madaba faces poor security in terms of managing climate- and water-related risks. The IUWSI framework assists with a rational and evidence-based decision-making process, which is important for enhancing water resources management in water-scarce cities.

A novel framework of multivariate modeling of water distribution network through 33 factorial design and artificial neural network

The water distribution network is largely affected by the change in the influencing factors, such as input pressure, demand and supply duration. The change in each parameter requires the extensive design of the network and the interactive effect of the influencing parameters are hardly explored. The main hurdles for the water providers lie in the absence of a prediction model, which can be used as a decision tool to assess the effect of the change in parameter and estimating the cost for the changed scenario. The present study developed a novel framework based on the artificial neural network for multivariate prediction modeling taking the response as the cost of the pipe network. The application of the 3³ factorial design was used for the selection of the influencing parameters and outcome was taken as the input to the neural network model. The adequacy of the model was tested through error functions and analysis of variance. The low values of the error functions (0.0004-0.228) and high F value (162,442) and R² (0.999) established the significance of the model. The model can be used for predicting the cost of the changed scenarios and assessment of the optimal solution for the system variables.