Data‐Driven Assessment of Water Meter Errors for Replacement Planning

Accurate water metering is essential for reducing non‐revenue water (NRW) and supporting sustainable urban water management. This study proposes a data‐driven framework for assessing water meter performance and supporting replacement planning by integrating advanced metering infrastructure (AMI) data with semi‐supervised learning. Using case studies from two Chinese cities, an apparent metering error (AME) metric was developed by combining flow‐dependent error curves with actual usage patterns. A neural network model, trained on 136 mechanical meters and applied to 140,000 residential meters, showed that aging meters systematically under‐register water use (AME: −6% to −2%), mainly because of low‐flow measurement failures and prolonged service duration. Results indicate that optimized replacement strategies can reduce greenhouse gas emissions and operating costs compared with conventional practices. The proposed framework provides a scalable approach for utilities to improve apparent loss assessment and meter replacement planning.