Filling in the Blanks: Applying Data Imputation in Incomplete Water Metering Data

2024-10-29

2024 IEEE International Smart Cities Conference (ISC2)

AI Summary

To address missing time-series data in smart water meters caused by device failures—impeding real-time water network monitoring, leakage detection, and predictive maintenance—this study systematically evaluates four imputation methods (k-NN, MissForest, Transformer, and RNN) on real-world urban water network data, marking the first such benchmark. Results demonstrate that temporal modeling approaches—particularly Transformer and RNN—substantially outperform conventional statistical methods, owing to their capacity to capture dynamic water consumption patterns and intrinsic periodicities. The proposed imputation framework improves meter data completeness to 98.2%, enhances leakage detection accuracy by 18.7%, and reduces predictive maintenance scheduling error by 23.4%. This work establishes a reproducible, real-world data imputation benchmark and a practical implementation paradigm for IoT-enabled smart water management.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Addressing data gaps in water metering due to technical issues
• Comparing imputation methods to improve water data accuracy
• Enhancing leak detection and maintenance via reliable data imputation

Innovation

• Apply data imputation to incomplete water metering data
• Compare k-Nearest Neighbors, MissForest, Transformers, RNNs
• Enhance leak detection and predictive maintenance accuracy