Fresh water is scarce—only 2.5% of global water is drinkable. Ensuring its quality is more critical than ever, yet traditional monitoring methods struggle with real-time detection and classification of contaminants. The AQUA PENTA-EURIPIDES² project tackled this challenge by developing an AI/ML-powered digital twin of water networks.
Technological achievements
The AQUA project has delivered several advancements in water quality monitoring, focusing on real-time data processing, anomaly detection, and enhanced sensor technologies.
A key achievement is the development of an all-inone cloud-based data science platform for the water industry. Build around a user-friendly dashboard, the platform integrates high data loads and real-time streams, facilitating seamless data flow and analysis across various water quality monitoring systems. A network-based anomaly detection model is integrated that uses ML to identify and categorize anomalies within water distribution networks, providing early warnings and actionable insights for proactive responses. This data platform, enabled by Dimensions from HAL24K, uses data model ontology for water network data elaborated in this project, improving communication and data management and enhancing the application of AI and ML for more effective real-time monitoring and response capabilities. A complementary AQUA data platform is developed for local-node sensor data collection, processing and distribution. Combining sensor data, it uses a “fingerprinting” technique to classify water quality changes enabling water utilities to quickly identify and respond to contaminants. Finally, developments were made to local-node broadspectrum water quality monitoring systems EventLab and AquaTEC. EventLab is a next-generation monitoring tool using refractive index for enhanced water quality event detection. The integration of additional sensors reduced false positives and ensures more accurate and robust event detection. AquaTEC is a system that uses advanced ML and computer vision to monitor fish behavior, providing deeper insights into the biological effects of water quality changes. The project enhanced systems performance by refining its ML algorithms and integrating additional sensors, improving its ability to detect abnormalities in fish behavior and correlate with fluctuations in water conditions.
Market Potential
The innovations from the AQUA project have potential for market penetrationin the water sector, particularly in Europe and Asia-Pacific, where early collaborations and validations have already been established within the consortium. When these connections are successfully leveraged, the consortium is well-positioned to expand into other regions. The project outcomes offer scalable, cost-effective solutions tailored to market needs in localnode and network-level water quality monitoring. Despite the conservative nature of the water industry, the demonstrated AQUA-platform benefits create a solid foundation for gradual adoption and growth, especially in real-time monitoring scenarios. With the water quality monitoring market currently valued at $5.35 million and expected to expand at a CAGR of 7.1% to 2030, the market potential provides opportunities for the consortium of the project. Prospects for the developed concepts in the broader utility sector are to be explored further.
Societal & Economic Impact
This project addresses critical water quality challenges across regions with diverse needs. By enhancing real-time monitoring and anomaly detection, it strengthens safeguards against emerging risks from climate change, industrial discharge, and growing population demands. These advancements enable water utilities to identify and mitigate quality changes more rapidly, ensuring safer drinking water. The technologies also support sustainable water management, aligning with global efforts to reduce water stress and improve operational resilience.
Future Developments
Optiqua will continue testing its Next Generation EventLab system and review its business case for bringing the system to production. Additionally, Zweec aims to improve AquaTEC by utilizing anomaly detection and enhancing correlation between network type of physical sensors and AquaTEC alerts. Furthermore, HAL24K will expand its anomaly detection model to other networks and develop a water demand model on the AQUA platform. Finally, De Watergroep will advance AQUA’s methodology and ontology, aligning with data processes and promoting international utility cooperation.
Discover more in the AQUA Project Impact Summary.
PENTA & EURIPIDES² are Eureka Clusters operated by AENEAS.