AI-Driven Digital Twin of an Irrigation District: Integrating Real-Time Data, Hydraulic Simulation & Predictive Maintenance

Abstract

AI enables the digital twin concept for a district with real-time data integration, hydraulic modelling, and predictive maintenance, supporting district management, operations, and planning.

A digital twin for a district integrates real-time data from a multi-source sensor network with a hydraulic simulation model. Distinct layers fulfil the requirements of managers, operators, and policymakers. The AI-driven twin includes a sensing architecture, an ingestion pipeline that ensures time-synchronized, quality-checked time series, a scalable hydraulic model representing the physical system, and machine learning for asset health monitoring and anomaly detection. Five simulation scenarios explore the effect of real-time data on irrigation efficacy. The digital twin becomes a blueprint for predictive maintenance using failure-mode prediction, scheduling of maintenance windows through a cost-benefit analysis, and asset health risk scores.

Identifying risk areas is particularly valuable for districts with aging infrastructure. The design concept can be translated to other irrigation areas, such as those requiring salinity monitoring, and scaled up, enabling predictive maintenance of pumps, valves, and sensor networks.