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.
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.
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.
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.
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.
If successful, the project will result in a theoretical and practical basis for a generic framework, together with applied research tools, that could support decisions for the provisioning of future water services to more than two-thirds of the world’s population that is expected to inhabit our cities by 2050.