A microservice architecture for leak localization in water distribution networks using hybrid AI

Abstract

Up to 30% of all drinking water is wasted due to leaks in water distribution networks (WDNs). In times of drought and water shortage, wasting so much drinking water has a considerable environmental and financial cost. In this paper, we present a microservice architecture for leak localization in WDNs, where heterogeneous sources of data consisting of sensor measurements, Geographic Information System (GIS), and Customer Relationship Management (CRM) data are used to feed a leak monitoring solution which combines hybrid data-driven and model-based leak detection and localization methodologies. The solution is validated using in-field leak experiments in an operational WDN. The final leak probabilities are presented in a visualization dashboard. The search zone for most leaks is reduced to a few kilometers or less. For other leaks, the solution is able to indicate a larger search zone to reflect its higher leak prediction uncertainty. HIGHLIGHTS A microservice architecture for leak monitoring in WDNs is designed.; Sensor measurements and GIS and CRM data are used for leak localization.; A post-processing procedure is presented to combine leak location predictions of two hybrid model-based and data-driven methodologies.; The architecture is evaluated using real leaks in an operational WDN.;

Keywords

• hybrid ai
• hydraulics
• leak localization
• machine learning
• microservice
• water distribution network