CHARACTERIZATION OF ANOMALIES AND EVENT DETECTION IN WATER NETWORKS USING ARTIFICIAL INTELLIGENCE AND EDGE-COMPUTING TECHNOLOGIES

The project proposes a new monitoring paradigm based on the integration of high-precision fiber-optic sensors with advanced Artificial Intelligence models and edge-computing data processing. This combination will enable real-time identification of critical events such as leaks, infiltrations, unauthorized access, or structural damage, preventing losses, reducing maintenance costs, and improving the sustainability of the resource.

The main goal of ADRIATICO is to develop deep-learning models capable of detecting and characterizing complex anomalies from distributed optical signals, and to optimize these models so they can run directly on specialized hardware such as FPGAs. This approach will allow data to be processed where it is generated, drastically reducing the dependence on central servers and improving the system’s operational and energy efficiency. Throughout the project, the developments will first be validated in laboratory conditions and later in a relevant environment within an operational water network, ensuring their practical applicability and transferability.

As a key outcome, ADRIATICO is expected to contribute significantly to the modernization of the water cycle through technologies that support proactive decision-making and predictive maintenance. Thanks to the system’s ability to detect events accurately and early, water utilities will be able to reduce losses, optimize resources, and enhance the resilience of infrastructures that are essential for economic development and social well-being. Additionally, the project will generate important scientific and technological impact through publications, demonstrators, and new capabilities applicable to future developments.

The consortium behind ADRIATICO is composed of entities that combine industrial experience, technological capability, and research excellence: FIBSEN Monitorizaciones, S.L., which will lead the integration and validation of the solution thanks to its extensive experience in fiber-optic sensing applied to the water sector; theUniversitat Politècnica de València, through our research group CVBLab, contributing specialized knowledge in artificial intelligence and deep learning and taking responsibility for designing and training the predictive models that will detect anomalous events in water networks; and ARTIKODE Intelligence, S.L., which will contribute advanced tools for high-resolution data annotation and management, as well as the optimization of the models for execution on edge-computing hardware.