[Research Data] AI-Driven Assessment of Urban Water Meter Errors: A Sustainable Framework for Optimizing Resource Efficiency and AI-Driven Assessment of Urban Water Meter Errors: A Sustainable Framework for Optimizing Resource Efficiency and Reducing Carbon Footprint

Accurate water metering is critical for addressing non-revenue water (NRW) losses and advancing sustainable urban water management. This study proposes an AI-enabled framework to optimize meter replacement cycles, integrating advanced metering infrastructure data with semi-supervised learning to mitigate metrological errors and reduce lifecycle greenhouse gas (GHG) emissions. Through case studies of two Chinese cities (Y and W), we developed an Apparent Metering Error (AME) metric that quantifies water loss by synthesizing flow-dependent error curves and usage patterns. A neural network model, trained on 144 mechanical meters (MMs) and validated against 140,000 residential meters, demonstrated that aging MMs exhibit systematic under-registration (AME: −6% to −2%), driven by low-flow measurement failures and prolonged service durations. Results revealed that optimized replacement strategies could reduce GHG emissions and lower costs compared to conventional practices. The framework offers a scalable solution for global cities grappling with NRW and climate resilience challenges.