Data Recovery Methods for Sensor Data in Water Distribution Systems Based on Spatiotemporal Redundancy

With the rapid development of smart water distribution systems, real-time monitoring data from large-scale sensor networks plays a critical role in system optimization and failure prediction. However, sensor data quality is often compromised by faults and missing values, which significantly undermine the reliability of decision-making. To address this issue, this study proposes a spatiotemporal redundancy-based data recovery method for sensor data. Specifically, polynomial fitting and hierarchical clustering are employed to analyze the spatiotemporal redundancy inherent in sensor data, based on which a weighted feature matrix is constructed. This matrix is then subjected to dimensionality reduction to enhance data representativeness. Five models—Multivariate Polynomial Regression, Holt-Winters, Long Short-Term Memory Sequence-to-Sequence, Multi-scale Isometric Convolution Network, and Transformer—were systematically compared in data recovery tasks. Experiments were conducted using real-world data from a water distribution system in China, involving 58 pressure sensors and 36 flow sensors. Results demonstrated that the developed method achieved high accuracy alongside efficient computation, particularly excelling in scenarios with abundant spatial redundancy.