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

作     者：Kim, Hyunjun Jung, K. J. Lee, S. Jeong, E. H. 

作者机构：Pusan Natl Univ Dept Mech Engn 2 Busandaehak Ro63 Beon Gil Busan South Korea Flowork Lab Res & Dev Dept 8 Seojeon Ro Busan South Korea 

出 版 物：《AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY》 (Aqua Water Infrastruct. Ecosyst. Soc.)

年 卷 期：2024年第73卷第7期

页      面：1358-1371页

核心收录：

基　　金：Korea Planning & Evaluation Institute of Industrial Technology - Ministry of the Interior and Safety 

主　　题：data acquisition machine learning algorithm network anomaly detection operational insight pressure wave detection water distribution network 

摘      要：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. HIGHLIGHTS Developed a machine learning (ML)-based system to dynamically monitor pressure in water networks. Utilized advanced Orion ML library algorithms for real-time anomaly detection. Enhanced operational efficiency by optimizing data acquisition rates. Demonstrated effectiveness in pilot and real-world water distribution settings. Integrated Orion ML library with advanced algorithms to improve infrastructure resilience.