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A new alarm generation concept for water distribution networks based on machine learning algorithms

Paper presented at HIC 2014, 11th International Conference on Hydroinformatics, "Informatics and the Environment: Data and Model Integration in a Heterogeneous Hydro World", New York City, USA, August 17 - 21, 2014
: Kühnert, Christian; Bernard, Thomas; Montalvo Arango, I.; Nitsche, R.

Volltext urn:nbn:de:0011-n-3239505 (626 KByte PDF)
MD5 Fingerprint: 7633e592ccffb1eee3b26dcc1db0a41b
Erstellt am: 30.1.2015

2014, 8 S.
International Conference on Hydroinformatics (HIC) <11, 2014, New York/NY>
Vortrag, Elektronische Publikation
Fraunhofer IOSB ()

Water Distribution Networks are complex systems containing a large amount of sensors placed in the network. An important task of water quality sensors is to give information to the operators if a contamination has occurred in the network. Generally, the overall analysis of the sensor data is performed manually by the operators since data-driven alarm generation systems for protecting a network in real time are not established at water utilities. This has several reasons: At first, the parameterization of these modules is often very complex and time consuming. Secondly, in most cases these systems generate too many false positive alarms due to special operational actions like sensor calibration or flushing of pipes. In this paper an approach is presented which addresses both problems. First, a self-configuring alarm generation module is proposed which only needs a few parameters to be set. Next, using the module, it is shown that the amount of false positive alarms can be reduced, if known events are used for training the module. This approach is proved in experiments performed on a laboratory plant.