Streaming Data Processing & Analytics on Water Supply Systems

The operation and management of water supply systems (WSS) have become increasingly difficult and complex. WSSs currently collect a large amount of data signals (e.g., pressure, flow) at different time frequencies in order to obtain useful information about the current and future state of the system. However, there are several knowledge gaps and challenges related to data flow, including: i) poor data quality and lack of tools to process and integrate data from different sources; and ii) the effective use of a huge amount of collected and stored data. These gaps highlight the need to improve data governance practices and use the collected data more effectively to increase the resilience and efficiency of the WSS.

The main objective of the StreamWater project is the efficient processing of real-time data from large water data streams to improve daily operation and management. The data streams will occur with different frequencies and synchronization intervals. In addition, it also aims to create more accurate data-based techniques for the detection and real-time location of anomalous events in the SAA. Finally, a roadmap will be developed with the lessons learned during the project, allowing other national and international water supply companies to replicate and improve the methodology for processing and analyzing the water data stream.