Integrating IoT and AI for Smart Water Management: Enhancing Urban Water Networks with Real-Time Monitoring and Digital Twin Technology

As global water resources face unprecedented challenges from population growth, climate change, and urbanization, innovative technologies are essential for sustainable water management. This study explores the application of Internet of Things (IoT) and Artificial Intelligence (AI) within Smart Water Management Systems (SWMS), highlighting their transformative potential in urban water networks. IoT-enabled devices offer continuous real-time monitoring of water parameters, providing a wealth of data that AI algorithms can analyse to optimize water distribution, detect leaks, and manage water quality. The implementation of Digital Twin technology allows for the simulation and analysis of various water management scenarios, enhancing decision-making processes and operational efficiency. This research presents case studies demonstrating the effectiveness of IoT and AI in predicting water demand patterns, identifying system failures, and improving overall water management resilience and sustainability. The integration of these technologies not only reduces operational costs but also enhances environmental protection, aligning with the goals of sustainable development and risk mitigation in water resource management. Our findings contribute to the ongoing discourse on smart water grids, showcasing how IoT and AI can be effectively integrated into traditional water management infrastructures. This study provides a comprehensive roadmap for future advancements in water technology, emphasizing the importance of innovative approaches in addressing the complexities of modern water management​.