Smart Water Futures: designing the next generation of urban drinking water systems

Informazioni relative al progetto

Water-Futures

ID dell’accordo di sovvenzione: 951424

DOI

10.3030/951424

Data della firma CE 12 Marzo 2021

Data di avvio 1 Agosto 2021

Data di completamento 31 Luglio 2027

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 9 982 320,00

Contributo UE

€ 9 982 320,00

Coordinato da

UNIVERSITY OF CYPRUS
Cyprus

Periodic Reporting for period 2 - Water-Futures (Smart Water Futures: designing the next generation of urban drinking water systems)

Periodo di rendicontazione: 2023-02-01 al 2024-07-31

Sintesi del contesto e degli obiettivi generali del progetto

Water-Futures aims to answer a key question: “How should the world achieve the provisioning of high-quality water services in the future while facing severe climate, economic and population pressures, under considerable (deep) uncertainty?”
The project is focused on developing a new theoretical framework for designing smart water systems. This new framework will enable making decisions about drinking water allocation, and infrastructure development, in a way that conforms with the principles of social equity, economic efficiency, and environmental resilience. This aligns with the Sustainable Development Goals of the United Nations Agenda 2030, which aims to promote sustainable development worldwide. These next-generation water systems will utilize new technologies to integrate real-time monitoring and control with long-term robustness and flexibility.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

Since the start of this project in August 2021, we've made significant progress in several key areas:

- Optimizing Water Systems for the Future: One of our main goals is to design and manage water systems more intelligently. Traditional methods often overlook future changes, so we introduced 'operational flexibility' for adaptable planning. We're also investigating how climate change-driven temperature increases may impact water quality.
- Improving Monitoring and Control: We're enhancing water systems with real-time sensors for leak, contamination, and equipment failure detection. We've organized a competition to spur innovation in leak detection methods. Additionally, we've introduced the "WaterSafe" benchmark to improve fault diagnosis methods.
- Explainable Machine Learning: We're using the power of artificial intelligence to make sense of the vast amount of data these systems generate. We've developed algorithms that can learn from this data and help humans make better decisions. We've also figured out ways to make these algorithms more Interpretable, so experts can trust and use them.
- Sustainable and Ethical Decision-Making: Water isn't just about pipes and pumps; it's about people's well-being and the environment. We're creating a new framework that considers the ethics and well-being of everyone involved in these water systems. This involves advanced modeling and understanding how different types of uncertainty affect decision-making.

So, what are our most significant achievements so far?

- We've shown that Reinforcement Learning, a type of artificial intelligence, can help us design better water systems, even when we're not sure what the future holds.
- We've developed a novel algorithm called ActiSiamese, which can spot problems in water systems quickly and accurately.
- Our work at the intersection of AI and Online Learning is making it easier to learn from the massive amounts of data these systems produce.
- We've broken new ground by combining insights from behavioral and neuroeconomic studies to help us make better decisions about our environment.
- We've actively engaged with water authorities and companies to ensure that our research is practical and useful in the real world.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The Water-Futures project is all about pushing the boundaries of the design and operation of water distribution systems in a way that makes them more resilient, efficient, and ethical. Here's what we've achieved so far and what we expect to achieve by the end of the project:

Going Beyond the State of the Art:

- We've introduced a novel idea of using Reinforcement Learning to design water systems that can adapt to uncertain conditions. This is groundbreaking because it helps us create more flexible and efficient systems.
- Our ActiSiamese algorithm is efficient at spotting problems in water systems, and can quickly find issues like leaks or contamination, even in complicated situations.
- We're at the forefront of combining Artificial Intelligence and Online Learning to make sense of the huge amount of data our water systems generate. This means we can react faster to changes and keep our water safe.
- We're breaking new ground by using insights from behavioral and neuroeconomic studies to make better decisions about water resources. This approach takes us beyond the current state of the art in understanding how people value water and the environment.

Expected Results by the End of the Project:

- We aim to create water systems that are not just smart but also ethical. This means considering the well-being of everyone involved, from water utility workers to the communities they serve.
- Our project will produce tools and methods that water authorities and companies can use to make better decisions. Water utilities will be able to predict and prevent problems before they happen, saving both water and money.
- We'll continue to engage with water authorities and companies to ensure that our research is put into practice.
- By the end of the project, we hope to have a comprehensive framework for designing, managing, and valuing water systems that can be used around the world.

Water-Futures meeting at KIOS, University of Cyprus, Nicosia, Cyprus

Water-Futures meeting at KWR, Utrecht, The Netherlands