Assessing the Impacts of Failures on Monitoring Systems in Real-Time Data-Driven State Estimation Models Using GCN-LSTM for Water Distribution Networks

Water distribution networks (WDNs) are critical infrastructures that directly impact urban development and citizens’ quality of life. Due to digitalization technologies, modern networks have evolved towards cyber-physical systems, allowing real-time management and monitoring of network components. However, the increasing volume of data from monitoring poses significant challenges to accurately estimate the hydraulic status of the system, mainly when anomalous events or unreliable readings occur. This paper presents a novel methodology for state estimation (SE) in WDNs by integrating convolutional graph networks (GCNs) with long short-term memory (LSTM) networks. The methodology is validated on two WDNs of different scales and complexities, evaluating the SE of the sensors. The capability of the GCN-LSTM model was assessed during the last two months of the time series by simulating failures to analyze its impact on sensor readings and estimation accuracy. The smaller network showed higher sensitivity of the sensors to detect failures, while the larger one evidenced more challenges in SE due to the sensor dispersion. Overall, the model achieved low prediction errors and high coefficient of determination values between the actual and simulated values, showing good performance. Likewise, the simulated failures showed that replacing the missing data with the hourly mean of the last week significantly improved the accuracy of the predictions, guaranteeing a robust SE in the event of sensor failures. This methodology provides a reliable tool for addressing various network configurations’ operational challenges.