Machine Learning advancements for design of water and energy policies in a changing climate and society

Nov 1, 2022, 4:30 pm5:30 pm
A71 Louis A. Simpson International Building



Event Description


Andrea Castelletti is a professor of Natural Resources Management and Environmental Systems Analysis at Politecnico di Milano, Italy and a Academic Guest at the Department of Civil and Environmental Engineering, ETH Zurich, Switzerland. He received a MSc degree in Environmental Engineering and a PhD in Information Engineering from Politecnico di Milano in 1999 and 2005. He was visiting scholar at Cornell University, ETH Zurich, Lancaster University, and University of Western Australia. From 2007 to 2015 he was Adjunct Professor at the Centre for Water Research of the University of Western Australia. He is the head of the Environmental Intelligence for Global Change Lab at Politecnico di Milano.

Dr. Castelletti research interest includes water systems planning and control under uncertainty and risk, decision-making for complex engineering systems, big environmental data analytics and smart sensing, information theory and selection for environmental decision making. Dr. Castelletti is co-author of 2 international books on integrated water resource management, more than 180 publications in international journals, book chapters and conference proceedings. In 2009 is was awarded a senior fellowship by the Japanese Society for the Promotion of Science, in 2010 an Early Career Excellence Award by the International Environmental Modelling and Software Society, in 2013 the Italy-Canada Innovation prize, in 2016 the EFARRI award, and in 2018 he was Biennial Medalist for the International Environmental Modelling and Software Society.

Dr. Castelletti serves the scientific community as the chair of the EGU Program Committees on Water policy, management and operation and member of the ASCE/EWRI Environmental and Water Resources Systems Technical Committee (since 2011). He is the past chair and current deputy chair of the IFAC Technical Committee TC8.3 on Modelling and Control of Environmental Systems (2008-2014). He is Associate Editor of Water Resources Re- search, Journal of Hydrology, Environmental Modelling and Software, and Socio-Ecological Systems Modelling.


Advances in environmental data monitoring and earth systems’ modeling have increasingly allowed us to accurately reproduce physical processes and their interactions at multiple scales, improving our ability to inform water and energy systems policy design. Yet, many process-based models are limited in predicting complex dynamics, which are the key to strategic planning, such as the impact of extreme weather and climate events or the mutual influence between humans and earth systems. Our work has shown that advanced data analytics and Machine Learning offer new opportunities to better characterize and model coupled human-earth system processes in a world in transition, ultimately supporting more equitable and efficient decision-making processes. In this talk, we provide an overview of recent advances in data-driven modeling and control of human-water-energy systems and showcase how Machine Learning techniques can help (i) infer natural and anthropogenic drivers of observed hydroclimatic patterns and improve their predictability in space and time, (ii) understand and conceptualize the mutual influences between human behaviors and water-energy systems; and (iii) design strategic planning and management policies optimizing multiple and conflicting objectives with different dynamics and informed by heterogeneous information. We recognize that this is a rapidly evolving research field, and we will also stimulate discussion around key challenges, including modeling decisions under uncertainty, model explain ability, data and computational requirements, model scalability, and transferability.

  • Civil and Environmental Engineering
  • Center for Statistics and Machine Learning