This paper describes an ongoing work on the application of machine learning techniques in the domain of water distribution networks. This research is performed in the framework of the European project WATERNET, whose aim is to develop a system to control and manage water distribution networks. WATERNET is composed of a supervision system, a distributed information management subsystem, an optimization subsystem, a water quality monitoring subsystem, and a simulation subsystem. In addition to these components, a machine learning subsystem is included to extract knowledge from historical data and improve the performance of the water management system. This paper is focused on the approach and methodology followed for the development of the machine learning subsystem. The basic raw material for this work are historical data from a Portuguese water distribution company that has 45 water stations and some of them with six years of data collected every five minutes. The paper also shows the first results obtained, discusses difficulties found in the performed experiments and introduces an architecture based on qualitative models/causal relationships to ease the process of knowledge extraction from the historical data and the assessment of the extracted knowledge.
- Machine learning systems
- Water distribution networks