Integration Of Smart Technologies And Digital Twins In Hydraulic Systems For Real-Time Water Management And Leak Detection
Annotatsiya
Increasing demand for advanced water infrastructure to ensure resource security in the future has already driven the integration of smart technologies into hydraulic systems. In addition to this challenge, urbanization pressure is expected to increase the complexity of leak detection (through increased pipeline density and the occurrence of hidden losses) for utilities considerably. To achieve the objectives: (1) the feasibility of digital twin implementation by conducting a multi-method analysis of leakage dynamics using regression, AHP, and SEM; (2) the sensor-based dependence of real-time water data; and (3) the predictive modeling of anomaly signals by statistically validating the quality of the digital simulation model. We examined tolerance variance and its implications in leak detection accuracy of distant distribution zone groups (industrial, residential, and agricultural) with different consumption profiles but under similar climatic pressures, as they have been exposed to the same constant network load conditions for a long time. Sites in Tashkent, Samarkand, and Bukhara were selected to represent regional typologies, with overlapping supply structures reflecting differences in infrastructure age to enable comparative assessment. Leakage risk was examined using standardized error metrics and path analysis statistics, and hierarchical clustering analysis was implemented to determine the latent structure of the observed leakage patterns. The overall SEM model reflected a strong correlation between sensor signal data and system anomalies in the pressure vs. flowrate + consumption comparisons (p < 0.01). After sensitivity analyses by AHP and regression, a distinctive difference was also revealed in the reaction time and failure diagnosis capability, as well as in the decision-making efficiency (R² = 0.89) thresholds. These include the influence of real-time analytics on capacity to predict burst events (e.g., transient pressure spikes), informed selection of critical node sensors able to complete crucial parts of their network coverage development to avoid time-lag-related failures, inducing, for example, delays in valve control in late development zones, and the detection of potential inefficiencies in pipeline management protocols. Long-term sustainability may be associated with adaptive calibration of predictive systems, especially in aging networks and rapid-growth districts, as well as in semi-arid regions.