Project Intelligence Augmentation Ecosystem for analysts of water distribution networks (WaterPrime)

Logo projektu WaterPrimeThe Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences, in consortium with AIUT sp. z o.o. company, started the implementation of a three-year project, "Intelligence Augmentation Ecosystem for analysts of water distribution networks - WaterPrime" (2021-2023), subsidised from the Smart Growth Operational Program 2014-2020 of the European Regional Development Fund by the Intermediate Body: National Center for Research and Development.

The project concerns an economically, socially, and ecologically important area of ​​reducing water supply networks' water losses. The technical infrastructure of water distribution systems requires constant supervision, including telemetry solutions, most recently as the Internet of Things (IoT). Significant amounts of data collected during monitoring need analysts to process and interpret, for example, to detect anomalies such as water leaks or equipment failures. Their decisions are critical to maintaining the efficiency of the infrastructure. The analyst or dispatcher uses data analysis tools to obtain knowledge about the infrastructure and its current condition. The role of analysis tools in this situation is to provide maximum support for such a person, both by providing complex data analysis tools - the so-called artificial intelligence - as well as towards intelligent assistance and assistance, including in the area of ​​increasing their competences and improving the speed and accuracy of decisions.

The project aims to create a platform of software tools supporting the analysis of water distribution network data. Thanks to the platform's use, it will be possible to increase the effectiveness of leak detection and instances of imbalance and other non-conformities that may signal events leading to technical problems in the future.

The R&D work in the project focuses on three main research topics:

  • • machine learning methods closely integrated with the data acquisition and storage layer,
  • • application of Augmented Intelligence, learning by observing the work of analysts, facilitating the selection and parameterization of their work tools,
  • • solutions for controlling the IoT layer's configuration using the LoRa protocol, using a unique method of aggregation and delegation of tasks to reduce global energy consumption and extend sensors' working time.