The goal of this project is to develop a reliable and unified solution, which will enable cost-efficient distributed sensor-based monitoring of hydraulic and hydrological states of the urban water cycle providing data for knowledge and information generation. Based on collected sensor measurements and precipitation, the project utilizes machine learning techniques (probabilistic models and auto-encoders) for the development of virtual sensors as well as for anomaly detection in both the physical sensors and in the water system as a whole.