Artificial Intelligence, Digital Twins, and Cyber-Resilience in Water Distribution Systems: A Critical Narrative Review with a WDT Benchmark Case Study

Water-infrastructure digitalization increasingly couples hydraulics with sensing, communication, artificial intelligence (AI), and supervisory control, while digital twins support operational decisions. This article combines a critical narrative review with structured full-text evidence mapping of 50 water cyber-physical systems (CPS) studies and an accompanying reproducible Water Distribution Testbed (WDT) benchmark case study. The review synthesizes model-based, data-driven, and physically informed methods across detection, localization, mitigation, and recovery, distinguishing anomaly evidence, cyber attribution, and closed-loop resilience. The WDT case study compares nominal-only detectors and alarm policies under a common chronological protocol. Across five primary neural runs, cumulative policies increase sample-level recall but sustain alarms and detect fewer event onsets than the fixed-threshold policy. These numerical findings characterize the declared protocol and evaluated records; they do not establish a general ranking of detection algorithms. They motivate interpreting classification metrics alongside event performance and held-out nominal alarm burden. Priorities include trustworthy digital-twin updating, causal localization, uncertainty-aware intervention, independent validation, and verified recovery for intelligent water-infrastructure operation.