This course examines urban water and networked systems, with emphasis on their modeling, analysis, management, and system-level performance. Topics include urban watersheds and hydrologic processes; drinking-water distribution, urban drainage, and wastewater networks; reliability, resilience, risk, asset management, and interdependencies across natural and engineered systems; foundational graph theory and complex-network analysis; and computational methods including supervised and unsupervised machine learning, neural networks, and graph neural networks. Students will use Python to analyze urban water and other networked natural-engineered systems and complete a research-oriented project integrating domain knowledge with data-driven and AI-based methods.