AI-based acoustic leak detection in water distribution systems

Abstract

Water loss in distribution networks known as Non-Revenue Water (NRW) is one of the major challenges facing water utilities. In a densely populated city, the acoustic listening method manually conducted by waterworks operators during routine leak pinpointing tasks is vital for NRW reduction. However, this method is considered to be typically labor-intensive, skill-dependent, non-systematic, and sometimes imprecise due to fatigue and inexperience of newly trained staff. This paper presents the development of an AI-based water leak detection system with cloud information management. The system can systematically collect and manage leakage sounds and generate a model used by a mobile application to provide operators with guidance for pinpointing leaking pipes. A leakage sound collection and management system was designed and implemented. Leakage sound datasets were collected from some multiple areas of the Metropolitan Waterworks Authority. Machine learning algorithms including Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Support Vector Machine (SVM), were developed and compared. The results show that the DNN performed better than SVM and as well as CNN, but with less complex structure. DNN was then selected to generate a model used in field trials for pinpointing leakage by novice operators. The field trial results show that the accuracy of the system is above 90% and the results were similar to those conducted by experts.

Keywords

• Leak detection system
• Cloud management
• Deep neural network
• Machine learning