Exposure of citizens to potential disasters has led to vulnerable societies that require risk reduction measures. Drinking water is one main source of risk when its safety and security is not ensured. aqua3S combines novel technologies in water safety and security, aiming to standardise existing sensor technologies complemented by state-of-the-art detection mechanisms. On the one hand sensor networks are deployed in water supply networks and sources, supported by complex sensors for enhanced detection; on the other hand sensor measurements are supported by videos from Unmanned Aerial Vehicles (UAVs), satellite images and social media observations from the citizens that report low-quality water in their area (e.g. by colorisation), creating also social awareness and an interactive knowledge transfer. Semantic representation and data fusion provides intelligent DSS alerts and messages to the public through first responders’ mediums. The proposed technical solution is designed to offer a very effective detection system, taking into account the cost of the aqua3S platform and targets at very high return over investment ratio. A strategy for the insertion of aqua3S solution into the market is designed towards the standardisation of the proposed technologies and the aqua3S secure platform.

The WATERVERSE mission is to develop a Water Data Management Ecosystem (WDME) for making data management practices and resources in the water sector accessible, affordable, secure, fair, and easy to use, improving usability of data and the interoperability of data-intensive processes, thus lowering the entry barrier to data spaces, enhancing the resilience of water utilities and boosting the perceived value of data and therefore the market opportunities behind it. WATERVERSE takes a holistic, interdisciplinary approach in the water domain, blending together complementary competencies of 17 partners located in 10 EU countries, representing the water domain with Research organisations (including social sciences experts), water utilities, water domain technology providers and innovation companies, as well as the technical community that is driving the development of data spaces, thus increasing the resilience of the water sector and water utilities, as a whole. The project will: (a) Actively engage end-users and stakeholders to assess the main gaps and challenges the water sector must overcome to effectively be part of and contribute to quality European data spaces; (b) Identify, extend, and integrate a wide set of data management tools to implement the WDME, based on FIWARE (www.fiware.org) Building Blocks and comprising tools and methods to ensure security and energy efficiency of the whole WDME; (c) Setup and demonstrate the WATERVERSE WDME in real environment with relevant and diverse case studies involving water sector stakeholders from 6 countries (Cyprus, Spain, Germany, the Netherlands, Finland, United Kingdom); (d) Set clear and measurable indicators for assessing FAIRness of data in water-related data spaces; (e) Ensure the viability and sustainability of the WATERVERSE WDME, as well as its replicability, scalability and business applicability.

intoDBP will create innovative tools and strategies to improve water quality management for safe human use and a healthy environment. It focuses on catchment protection and forecasting, transformative drinking water treatment, and real-time monitoring to combat the effects of climate and global change. In particular, intoDBP focuses on pollution and risks related to disinfection by-products (DBPs). By developing and applying advanced, integrated, and cost-effective sensors and analytical methods, intoDBP will expand knowledge on water quality and DBP precursors to better understand its formation and human exposure in Europe. intoDBP monitoring results will feed into numerical forecasting tools to predict source water changes and formulate climate change adaptation pathways at catchment and treatment scale. intoDBP also develops transformative options for advanced and cost-effective upgrade of water treatment and disinfection. In the intoDBP consortium researchers, small and large enterprises, communication experts and public services join forces to generate interdisciplinary solutions, that will generate a renewed perspective of drinking water surveillance, support decision-making and governance, and increase system resilience. intoDBP will implement and validate its cross-cutting products in four complementary case studies from three European countries where compliance with DBP regulation currently is an acknowledged challenge. The direct and visible positive impact of intoDBP in the case studies will foster rapid product adoption at a European and global scale, thus strengthening Europe’s position and role in the global water market. Reaching out beyond the water sector itself, intoDBP will directly engage society through surveys to analyse exposure to DBPs, collect data about catchment protection initiatives, create awareness and promote sustainable consumer behaviour such as reducing bottled water consumption.

Machine learning methods operate on formal representations of the data at hand and the models or patterns induced from the data. They also assume a suitable formalization of the learning task itself (e.g., as a classification problem), including a specification of the objective in terms of a suitable performance metric, and sometimes other criteria the induced model is supposed to meet. Different representations or problem formalizations may be more or less appropriate to address a particular task and to deal with the type of training information available. The goal of LEMUR is to create a novel branch of machine learning we call Learning with Multiple Representations. We aim to develop the theoretical foundations and a first set of algorithms for this new paradigma. Moreover, corresponding applications are to demonstrate the usefulness of the new family of approaches. We regard LEMUR as very timely, as LMR algorithms will allow to flexible representations (e.g., suitable for explainability, fairness) with diverse target functions (e.g., incorporating environmental or even social impact) so as to make the induced models abide by the Green Charter and trustworthy AI criteria by design. We will focus on learning with weak supervision because it addresses one of the major flaws of modern ML approaches, i.e., their data hunger, by means of weaker sources of labelling for training data. The outcome of the DN will be a set of 10 experts trained to implement the third and subsequent waves of AI in Europe. The highly interdisciplinary and intersectoral context in which they will be trained will provide them with research-related and transferable competences relevant to successful careers in central AI areas.