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Innovation and Impact

SAFEWATER will represent a significant step forward both in terms of scientific and technological impact – bringing new tools for the detection and management of water security. Additionally at the societal and economical level the impact of enhancing the real-time response capacities to drinking water security crises, whether accidental or malevolent will minimise the negative impact of such situations on both the relevant society and its economy.




Developing a global generic solution for the management of drinking water security and safety.

Provide an affordable integrated solution for the detection and management of water security crises.

Overall significantly improve the security and safety of drinking water.

Developing new CBRN sensors, virtual sensors, and domestic sensors with unprecedented stability and at reduced cost.

Provide recommendations on the specific use of sensors based on the user network and water quality profile. Benchmarking will be carried out by the use of algorithms analysing historical data.

Enhance the capacity of monitoring systems allowing for real-time detection of the presence of CBRN contaminants in the water supply system and also expanding the range of contaminants that can be detected.

Designing improved water management models by integrating learning algorithms for the detection of abnormal behaviour of large water drinking systems into the DSS design.

Enable the real-time ranking of the severity of alerts and evaluation of the propagation and contamination for better preparedness, response and management of the crisis for large municipalities

The models that will be developed for response and recovery purposes will have a major impact in the containment of contamination in a water supply network and on the reduction in time and means needed for the recovery of a contaminated water supply network by significantly reducing the consequences of an event.

Defining result interpretation models, by performing frequency and retro-active analysis for abnormal situations on the data collected.

Defining spatial detection model based on the combination of hydraulic and statistical models using water quality historical data from water distribution systems.

Developing online simulators which are calibrated automatically (both hydraulic and transport model), and an Event Detection Training Tool.

Developing the concept of domestic-sensors and virtual sensors.

Extend the event detection models to be based on thousands of low cost sensors and not on several more expensive sensors (40-80) located at different points of the network in order to obtain reliable knowledge of water quality as close to the end user as possible.