Segmenting Multivariate Time Series of Water Flow : a Prior Tool for Contamination Warning Systems

International audienceDrinking Water Distribution Networks (WDN) are critical infrastructures exposed to the risk of accidental and intentional contaminations. To ensure protection of drinking water, there is an important need to design automatic and secure Early Warning Systems (EWS). Online monitoring of water quality into a WDN is a challenging problem due to the complexity of hydraulic networks. Conventional detection methods deal with specific contaminants and usually assume a stationary state of the WDN meanwhile such problem is hardly addressed when operational conditions are changing. This paper introduces a generic methodology based on a temporal analysis in order to extract prior knowledge for warning detectors. Frequent types of operating period are extracted and for each period, upstream / downstream relationships into the WDN can be found. The procedure is fully data-driven and prevents to use heavy hydraulic-quality simulations during the monitoring stage. In fact, the method can be used as a preprocessing step by any detector in order to help dealing with multiple quality sensors and to avoid false alarms due to operating changes. The proposed approach is illustrated on a large real-world network in France and the experimental results are very promising