Early detection of events in sectorized water networks with Machine Learning techniques

Early detection of events in sectorized water networks with Machine Learning techniques

INCLAM and CIMNE promote the project SMILER.

WatEner is proud to share the news of the presentation of the paper “Adaptive algorithm for water loss estimation in networks based on advanced analysis of Minimum Night Flow (MNF)” undertaken by David J. Vicente of CIMNE at the Water Loss 2019 conference held last September in Bucharest.

During the event, organized by the ARA (Romanian Water Association) and belonging to IWA regional events on water loss, the latest developments, strategies, techniques and applications of international best practices in the management of Non-Revenue Water have been presented and discussed.

The presented study focuses on the research projectSMILER*” promoted by INCLAM in conjunction with CIMNE (International Centre for Numerical Methods in Engineering). The project’s primary goal is the development of a system for the early detection of anomalous events associated with bursts and leakage. The study is directed towards water utilities with sectorized networks and is based on data analysis using Machine Learning techniques.

During the development of the project, WatEner has contributed with its knowledge and has made available its platform for the efficient management of drinking water networks as a consolidated IT solution.

A plan for the analysis of the results has already been set in motion by the entities involved. Results will be presented on specialized forums and congresses at both national and international levels.

The next presentation by INCLAM and CIMNE of the SMILER Project results on the national ground will take place at the VI edition of the Water Engineering Conference (JIA) to be held in Toledo on 22nd to 25th October 2019.

* Project RTC-2017-6324-5 co-financed with FEDER funds, the Ministry of Science and Innovation and the State Research Agency

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