A fault sensitivity analysis for anomaly detection in water distribution systems using Machine Learning algorithms

Abstract

The introduction of Machine Learning in large scale utility networks extends the room for improvement in the quality of service and maintenance costs. The ever expanding network of smart meters allows for a more accurate estimation of the state of the water distribution systems, at the same time requiring modern data processing solutions. By fusion with the more traditional approach in this field of research it is possible to enhance the existing capabilities for network analysis and to extend the algorithms to the level of cognitive abilities that form a basis for more efficient decision support system. In this paper we extend the fault sensitivity analysis for water distribution systems with the insights provided by state-of-the-art Machine Learning algorithms for data clustering and anomaly detection.