Rapid response to pressure variations in water distribution networks through machine learning-enhanced data acquisition

This study investigates rapid dynamic pressure variations in water distribution networks due to critical incidents such as pipe bursts and valve operations. We developed and implemented a machine learning (ML)-based methodology that surpasses traditional slow cycles of pressure data acquisition, facilitating the efficient capture of transient phenomena. Employing the Orion ML library, which features advanced algorithms including long short-term memory dynamic threshold, autoencoder with regression, and time-series anomaly detection using generative adversarial networks, we engineered a system that dynamically adjusts data acquisition frequencies to enhance the detection and analysis of anomalies indicative of system failures. The system's performance was extensively tested using a pilot-scale water distribution network across diverse operational conditions, yielding significant enhancements in detecting leaks, blockages, and other anomalies. The effectiveness of this approach was further confirmed in real-world settings, demonstrating its operational feasibility and potential for integration into existing water distribution infrastructures. By optimizing data acquisition based on learned data patterns and detected anomalies, our approach introduces a novel solution to the conventionally resource-intensive practice of high-frequency monitoring. This study underscores the critical role of advanced ML techniques in water network management and explores future possibilities for adaptive monitoring systems across various infrastructural applications.