用数据填补技术修复智能水表缺失数据，提升漏水检测与维护预测精度。
Filling in the Blanks: Applying Data Imputation in incomplete Water Metering Data
- 对比多种填补方法，选最优方案恢复缺失用水数据
- 填补后漏水检测准确率显著提升，可靠性增强
- 适合水务管理、智慧城市建设相关研究人员参考
本文研究基于真实物联网水网监测部署数据，应用近期数据填补技术改善智能水表在供水管网监控中的应用。尽管智能水表产生详尽数据，但因技术问题导致的数据缺失会严重影响运营决策与效率。通过比较k-近邻、MissForest、Transformer和循环神经网络等多种填补方法，结果表明有效数据填补可显著提升用水数据的分析质量，增强漏损检测与预测性维护调度的准确性与可靠性。
原文摘要 · Abstract (English)
In this work, we explore the application of recent data imputation techniques to enhance monitoring and management of water distribution networks using smart water meters, based on data derived from a real-world IoT water grid monitoring deployment. Despite the detailed data produced by such meters, data gaps due to technical issues can significantly impact operational decisions and efficiency. Our results, by comparing various imputation methods, such as k-Nearest Neighbors, MissForest, Transformers, and Recurrent Neural Networks, indicate that effective data imputation can substantially enhance the quality of the insights derived from water consumption data as we study their effect on accuracy and reliability of water metering data to provide solutions in applications like leak detection and predictive maintenance scheduling.
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