Innovative tools for the detection and mitigation .. (SAFEWATER)
Innovative tools for the detection and mitigation of CBRN related contamination events of drinking water
Start date: Oct 1, 2013,
End date: Dec 31, 2016
SAFEWATER will develop an affordable global generic solution for the detection and management of drinking water crises resulting from CBRN contamination. SAFEWATER addresses the key drinking water incident management challenges at large, and in particular, the current shortcomings related to the contamination of water networks by CBRN agents - the lack of effective detection capacities, contamination warning systems, and decision support and management tools.SAFEWATER will start from best-of-breed technologies, including an EPA challenge winning event detection system. From this, the project will develop a dedicated DSS for the real-time support of decision makers, which comprises cutting-edge algorithms based on:• Improved water management models for the detection of abnormal behaviour in drinking water systems; as well as the prompt treatment of data from various sources, improving contamination alert systems of large water drinking systems• Result interpretation models to enable the real-time ranking of the severity of alerts and for the prompt identification of recovery measures• Spatial detection models to determine the contamination’s source and spreadThe functionalities of a leading Event Management System will be expanded by introducing beyond the state of the art online simulation capacities, allowing users to have a close to real-time view of the network’s behaviour.New sensors will be proposed for online biological and radioactive water quality measurements. SAFEWATER will improve sensor selection by carrying out benchmarking activities and develop an innovative detection approach based on “virtual sensors”, i.e. large networks of domestic sensorsThe project will test and validate the full SAFEWATER solution in three different scenarios, each situated in a different municipality corresponding to a different usage context and to specific security threats.
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