As large investments are made every year in maintenance, replacement, and expansion of the water infrastructure to better deal with the challenges faced, it is a necessity to ensure effective decision making for government officials and water utilities managers.

To help tackle this, the DONUT-project partners have worked together to develop a unified solution, which enables cost-efficient distributed monitoring of the hydrological and hydraulic states of the entire urban water cycle and provides data and knowledge about the system correlations. The project partners have taken a novel approach, utilising a network of simple, low-cost and low-energy sensors, combined with software sensors and machine learning to obtain a holistic overview of the water cycle, which can then be used across administrative boundaries to ensure a consistent basis for decisions on e.g., future investment.

More than 200 monitoring sensors were installed throughout the urban water system, from pressure monitoring in groundwater abstraction and distribution, to monitoring of water levels at a local stormwater management solution and in sewer systems. The results reveal that the total cost of ownership (TCO) can be lowered up to 90% compared to traditional monitoring approaches. Further value creation has been obtained, by releasing resources from data collection and analysis to problem solving and decision making for water utilities and municipalities.

Contributors: Innovation Fund Denmark, Dryp, InforMetics, MONTEM, Aalborg University – Department of the Built Environment, Aalborg University – Department of Computer Science, VCS Denmark, Aarhus Municipality and Aarhus Vand.