Reuse of wastewater has been widespread in this era to support the water sustainability process. Therefore, treated wastewater should be conveyed to suitable places and adopted for different uses. This study presents an empirical relationship between the Darcy-Weisbach and Hazen-Williams equations for four types of pipe material (ductile iron, GRP, concrete, and plastic) by using WaterCAD Version 8i. Two hydraulic models were developed to estimate the head loss in pipes by using different diameters: first, using pipe diameters from 800 mm to 1,200 mm for a flow rate of 1.16 m3/s, second, adopting pipe diameter from 1,600 mm to 2,000 mm for a flow rate of 4.63 m3/s. The study results are the head loss values obtained from the Darcy-Weisbach and Hazen-Williams equations, which were used to correlate them using IBM SPSS Statistics. The correlation coefficient between both equations turned out to be 0.991, 0.990, 0.990, and 0.990 for ductile iron, GRP, concrete, and plastic pipe materials. Additionally, the relationship between head loss and pipe diameter is negatively proportioned for both equations. Also, both head loss equation results are the same. The head loss values in the Darcy’s equation were higher for ductile iron and GRP materials, while being lower for concrete and plastic materials for both models. Selecting concrete or plastic pipes to convey treated wastewater is better than other pipe materials. Another conclusion is that the pipe diameter affects the head loss magnitude irrespective of the kind of equation whether Darcy-Weisbach or Hazen-William equation. Finally, this relationship is very useful for designers in converting the head loss values obtained using these equations.

Traditional water quality monitoring methods face significant limitations, including delayed data acquisition, high operational costs, and inadequate spatial and temporal resolution, which hinder timely responses to contamination events. This systematic review addresses these gaps by evaluating the transformative role of artificial intelligence (AI) in revolutionizing monitoring practices through two novel mechanisms: (1) enhanced multivariate data fidelity via Internet of Things (IoT)-sensor networks and satellite remote sensing, and (2) predictive modeling precision using machine learning (ML) algorithms. By synthesizing 1,032 studies (2011–2025), we demonstrate that AI-driven systems achieve 94% accuracy in prediction and reduce field sampling costs by 60% through Landsat 8 satellite integration. Our analysis reveals a 13-fold increase in AI adoption since 2011, with innovations such as adaptive neuro-fuzzy inference systems (ANFIS) and deep neural networks (DNNs) facilitating real-time anomaly detection and contamination forecasting. The novelty of this review lies in its dual focus—quantifying AI's scalability for global water security while critically addressing unresolved challenges in data standardization, model interpretability, and ethical governance. These findings offer policymakers actionable insights, advocating for hybrid frameworks that integrate AI with existing infrastructure to bridge urban-rural disparities in water management.

This paper aims to analyze how AI can revolutionize dam and water resource management, the problem areas such as climate change, growing population rate, and deterioration of infrastructure; these AI technologies, in turn, drive predictive analytics, learning, real-time monitoring, decision-making, and resource management, thereby benefiting engineers and policymakers, among other stakeholders. Demand forecasting, flood management, and smart water quality monitoring enhance resource management, disaster prevention, and eco-conservation. On numerous occasions, AI models outcompete traditional hydrological approaches in terms of accurate water level and inflow predictions. In addition, the combination of AI with IoT sensors means that potential and actual conditions of dams and water quality are constantly monitored to optimize maintenance programs and avoid incidents. Problems arising from data quality and availability, interpretability of models, and the requirement of being a competent technical person hinder its widespread use. Similarly, ethical and legal considerations such as privacy and responsibility pose challenges to integrating AI into current systems. Addressing these challenges is very important if the impact of AI is to be enhanced. As highlighted in this analysis, there is a need for multi-disciplinary non-kingdom collaboration and specific expenditure to deal with these constraints. Each requires a greater research effort to improve our abilities to advance from non-parametric approaches to new paradigms of predictive modeling, big data, and real-time decision support and to become more responsible stewards of the Earth’s limited water supplies. The outcomes highlight AI’s potential to change water management and improve its effectiveness and sustainability. This research underscores the importance of ensuring a sustainable and secure water supply in the future to enable the provision of water supply as stipulated in the global sustainable environment and structures’ agenda.

With an emphasis on Pump Station 1 (PS1) of the Basra Water Project (Open Canal) in Iraq, this study examines the essential hydraulic parameters of water pumping stations under transient flow situations. The study assesses the effects of routine operations, unexpected shutdowns, and surge tank installations on pressure stability and system flexibility using hydraulic modeling with HAMMER V8i. The findings show notable changes in pressure during brief occurrences. An abrupt shutdown without surge tank protection resulted in minimum pressures of 12.5 m in pipes L1 and L2, exposing them to hydraulic transient effects. The maximum pipe pressure under normal circumstances was 17.5 m (L3). Because of its exposure to low-pressure occurrences, the analysis identifies L1 as the most in-danger pipeline. It has been demonstrated that traditional operating procedures, which frequently ignore transient dynamics, increase the probability of service disruption and lead to inefficiency. In contrast, adding surge tanks reduces pressure variability and lessens the impacts of the water hammer, significantly increasing pressure stability, especially when three tanks are used. The results highlight how adaptable operational procedures are essential for employing and managing water delivery systems. According to the study findings, adding surge tanks improves durability and performance while lowering the risks of transient flow occurrences. This offers a guide for restructuring water pumping station operations.