Innovative Operating Methodology for Continuous Water Supply in Georgian Regions: Digital Twins and IoT Integration

Abstract

This research develops and validates a comprehensive, data-driven operational methodology designed to ensure continuous and secure water supply in the regional districts of Georgia. Addressing the systemic inefficiencies of aging infrastructure—specifically high Non-Revenue Water (NRW) losses and energy-intensive pumping—the study introduces an integrated framework centered on the "Digital Twin" concept. By establishing a real-time bidirectional link between physical assets and a dynamic hydraulic model, the methodology leverages a Wireless Sensor Network (WSN) to capture high-fidelity data on pressure, flow, and quality parameters (turbidity, pH, residual chlorine, and electrical conductivity).

The core innovation involves the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to transition from traditional reactive maintenance to proactive operational management. These algorithms function as "virtual sensors," enabling the prediction of system behavior in nodes where physical sensing is cost-prohibitive. The methodology was empirically tested within the Dedoplistskaro municipality, a region characterized by complex topography and significant geodesic elevation shifts.

Key results demonstrate that the integration of EPANET-based hydraulic simulations with real-time IoT data reduced the response time for contamination detection from a 24-hour laboratory cycle to less than 10 minutes. Furthermore, the implementation of automated pressure management and leak-detection algorithms led to a 15-20% reduction in water losses. The findings provide a scalable technological roadmap for the digital transformation of Georgia’s water sector, offering a universal solution for optimizing energy consumption, ensuring chemical safety

through automated dosing, and establishing a robust foundation for "Smart Water Grid" implementation in developing regional infrastructures.