Smart Water Management Systems: Integration of IoT Sensors and AI for Sustainable Urban Water Infrastructure

The rapid urbanization and climate change have posed significant challenges to urban water systems, including water scarcity, aging infrastructure, and inefficient resource management. This dissertation explores the integration of Internet of Things (IoT) sensors and Artificial Intelligence (AI) in the development of smart water management systems for sustainable urban water infrastructure. The primary objective is to investigate how IoT sensors can provide real-time data on water usage, quality, and flow, and how AI can optimize water management by analyzing this data to predict demand, detect anomalies, and reduce wastage. A systematic methodology was adopted, where IoT sensors were selected and set up to collect real-time data, and AI algorithms, including machine learning and predictive analytics, were used to process and analyze the data. The findings reveal that the integration of IoT and AI significantly improves water resource efficiency by enhancing leak detection, demand forecasting, and optimization of water distribution systems. This approach leads to substantial cost reduction, water conservation, and improved infrastructure resilience. The contributions of this research highlight the potential for implementing IoT and AI technologies to create smart cities that manage water resources sustainably and efficiently. The integration of these technologies offers a transformative solution to urban water management, addressing current inefficiencies while paving the way for future advancements in smart water infrastructure. Key Words: Smart Water Management, Sustainable Development, IoT Sensors, Artificial Intelligence, Urban Infrastructure, Real- Time Monitoring, Water Conservation