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.



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.



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.



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.



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.

This project will challenge current paradigms of:

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A separate treatment of short-term and long-term infrastructure management

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The validity of mathematical optimality principles for decision making in transitioning of urban water infrastructure systems under deep uncertainty;

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The ability of existing machine learning paradigms to provide explainable models based on spatio- temporal and networked data afforded by increased sensing capabilities in water systems in the monitoring and control of evolvable systems;

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Natural sciences and engineering ignoring the human factor and adopting a rational agent approach in decision making.


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.

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