OBJECTIVE 1
Transitioning of Urban Water Systems
To develop theory and application of robust and flexible decision support strategies to cope with deep uncertainty associated with urban water systems that evolve over time.
OBJECTIVE 2
Water Monitoring & Evolvable Control
To develop new real-time distributed monitoring and evolving control methodologies for water systems in order to support the ability to learn from experience acquired from other parts of the system, and to interact with uncertain human decisions, considering both short-term and long-term planning goals.
OBJECTIVE 3
Learning for Decision Making
To develop Explainable Machine Learning models in non-stationary environments for complex structured and networked data to seamlessly support human decision making for smart water systems by data-driven technologies.
OBJECTIVE 4
Rationality & Eudaimonia
To develop a methodology that integrates economic, social, ethical and environmental considerations, with direct relevance to UN Agenda 2030 into an interdisciplinary decision-support framework that will allow agent-based societal welfare maximization in the short, medium and long-run, under deep uncertainty.
OBJECTIVE 5
Integration & Validation
To design and implement an open-source toolbox that integrates the scientific outputs produced, and the demonstration of the different methodologies developed by the research team, in three urban water systems.
