Fresh water is a scarce resource essential for human survival. Globally, only 2.5% surface water is potable. This makes reliable water quality monitoring in water distribution networks very important. The major disadvantage of conventional water quality monitoring systems is that they are unable to determine and classify the type of contaminant present in the water. Therefore, novel advanced online sensor systems are needed utilizing new data analytics and classification technology.

The Aquatic Qualification with AI (or: AQUA) consortium, consisting of an experienced engineering company (NL), a data intelligence scale-up (NL), two tech-providers (NL, SG), and a large water company (BE), aims to achieve beyond-state-of-the-art real time water event detection and classification. Towards this end, advanced machine learning (ML) frameworks will be developed and integrated into two state-of-the-art sensor systems (EventLab and AquaTEC), and subsequently integrated in a single system (the AQUA-platform). The ML-frameworks will be developed to perform sensitive monitoring of physiologically relevant changes in water quality at local nodes or at the network node level, for both centralized and decentralized water production and distribution networks. The goal is to enable near-real time water quality monitoring, allowing water utility companies and industry to rapidly assess threat levels of adverse water quality events and perform the required actions for mitigation.

The annual global expenditure on drinking water production and distribution currently exceeds 180 billion USD. Within that market, the segment for water quality monitoring and analysis constitutes an estimated volume of 3.75 billion USD for 2020 and is expected to reach USD 4.68 billion by 2025. The novel ML-based sensor platforms will be exploited on the Singapore and Taiwan water quality monitoring markets in the short term and globally in the mid-to long-term.