An Edge-Based Digital Twin Framework for Industry5.0 Water Management

Modern infrastructure must evolve from reactive, centralised models towards proactive, resilient, and human-centric systems aligned with Industry 5.0. Digital Twin (DT) technologies have emerged as a promising enabler of this transition, but their widespread adoption remains constrained by complexity and cost barriers. This research addresses these challenges through the development and experimental evaluation of a modular, edge-based Digital Twin architecture for water utility management. The contribution lies not in the individual forecasting, sensing, or control technologies, but in their integration into a resource-constrained, closed-loop node and in the empirical characterisation of its prototype computing-hardware cost, computational requirements, storage demand, and physical actuation capability. The approach is demonstrated through a proactive rainfall prediction and hydraulic decision-support system. To support rainfall-informed control, the forecasting models were developed and evaluated using ERA5 reanalysis data from 2000 to 2026 as a consistent reference. The enhanced connected configuration, which incorporated the externally supplied weather-category input, achieved R2=0.8096 and RMSE =0.1454. When this input was withheld to represent the proposed reduced-input fallback configuration, the standalone configuration retained R2=0.4038 and RMSE =0.2574. During a separate 24 h prospective deployment evaluation, forecasts were generated in advance and subsequently compared with the corresponding ERA5 reanalysis record. During this monitored period, the deployed model reproduced rainfall timing and aggregate volume more closely than the physics-based API forecast. The resulting framework supports rainfall-informed, node-level hydraulic decision-making and autonomous pump actuation while maintaining operator visibility through a local SCADA interface. By performing meteorological telemetry processing, rainfall inference, water-storage-state assessment, data logging, and physical control locally, the architecture reduces dependence on continuous cloud connectivity for these operational functions. The results establish the technical feasibility of implementing predictive water-management functions within a low-cost, resource-constrained Digital Twin node. The prototype provides an experimentally characterised foundation for subsequent evaluation in larger hydraulic systems and future distributed, multi-node water-management applications.