Smart Water Systems: The Role of Technology and Engineering in Optimizing Urban Water Resources

Urban water supply management is constrained by water scarcity, pipe bursts, and unequal distribution. Traditional systems lack predictive capabilities, resulting in resource loss and high operating costs. AI, IoT, and Big Data-integrated smart water networks can monitor in real-time, detect leaks, and predict demand, making the system efficient and sustainable. This research investigates AI-based smart water networks to optimize urban water management. The paper aims to improve water demand forecasting, identify real-time leaks, evaluate the feasibility of IoT-based smart meters, and create an AI-based optimization system to reduce water losses and operational costs. A mixed-methods approach, integrating machine learning predictive modeling with qualitative measurements of engineering progress, was employed. Supervised learning algorithms analyzed real-time sensor data, historical data, and cloud-based analytical data, with the Random Forest Classifier supporting water management planning. The model achieved 100% accuracy, 100% recall, and an F1 score of 100% for leak detection. IoT-assisted smart water meters reduced non-revenue water losses by 23% and operational costs by 18%. With these insights, AI has maximized the efficiency of water distribution by optimizing resource utilization and sustainability. AI-enabled smart water networks have greatly improved urban water efficiency through loss reduction, efficient resource usage, and prompt decision-making. However, it is important to note that implementation faces barriers due to high costs and cyber risks; nevertheless, it remains an exciting prospect for ensuring sustainability in water management. Specific Contribution: The present study proposes an AI-based framework for predictive water demand forecasting and leak detection. Its implications for IoT-enabled smart meters and cloud computing are discussed. The cost-benefit analysis provides further support and suggests other avenues for future research, such as reinforcement learning and adaptive control mechanisms, to maximize the use of water resources.