AI-Enabled Digital Twins for Water and Power Critical

Infrastructure: A Cross-Sector Review of Deployment Maturity,

Cascading Risk, and Trustworthy AI

Esraa Walid1,* Lozan Shady1 Asmaa Elsayed1 Manar Mostafa1

1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt

Received: June 05, 2026 Revised: August 12, 2026 Accept ⋆e dC: orSreepspteomndbienrg a0u9th, or2026ABSTRACT

AI-enabled digital twins are moving from simulation-oriented representations toward operational decision systems for

critical infrastructure, but deployment evidence remains uneven and highly sector dependent. This review synthesizes

AI-enabled digital twins in water and power infrastructure using a structured narrative evidence base of 36 primary

application studies, balanced between 18 water and 18 power studies, together with recent reviews, interdependency

research, and governance standards. The review compares application domains, data environments, AI roles, synchronization

demands, validation settings, and decision authority across both sectors. Three analytical contributions

extend beyond application cataloguing: a six-level deployment-maturity model with observable criteria; a coupled

water–power framework for physical, cyber/data, spatial, and organizational cascading risk; and a trustworthy-AI

pathway linking explainability, uncertainty, human oversight, fallback, traceability, interoperability, and re-validation.

The evidence shows that water twins are strongly shaped by heterogeneous sensing, hydraulic/process uncertainty,

and site-specific transfer, whereas power twins more often face tight latency, topology change, stability margins,

and protection-adjacent decision requirements. Across both sectors, the dominant unresolved gap is not isolated AI

accuracy but sustained field evidence demonstrating synchronized physical linkage, uncertainty-aware decisions,

governed authority, cyber resilience, and lifecycle re-validation. The review therefore provides a cross-sector

basis for distinguishing promising AI models from deployment-ready digital twins and for prioritizing future

infrastructure-grade validation.

Keywords: Artificial intelligence Digital twins Water infrastructure Power systems Deployment maturity

Cascading risk Trustworthy AI

1. INTRODUCTION

Artificial intelligence-enabled digital twins are increasingly

being investigated as dynamic computational counterparts of

physical systems in which sensing, communication, virtual

representation, intelligent analytics, and operational feedback

are integrated within a continuously evolving physical–digital

relationship. Their significance extends beyond conventional

simulation because the digital representation can be updated

from observations of the physical system and used to interpret

current conditions, examine plausible future states, and support

decisions. This transition from static modeling toward

context-aware physical–virtual systems is being driven by the

convergence of intelligent sensing, connected infrastructure,