for Global Change
We are a research group based at the Department of Electronics, Information, and Bioengineering, Politecnico di Milano. Our research mission is advancing environmental decision-analytics for supporting human decisions in complex engineering systems including multiple actors and exposed to evolving multisectoral demands and global change. Our research fuses environmental, climate, and hydrologic disciplines with machine learning, optimal control, and evolutionary computation. This multidisciplinary mix yields innovative, flexible, and robust solutions facilitating participatory decision making processes by addressing the multifaceted complexity of environmental systems, including their nested interdependencies across stakeholders, processes, and policies at different spatial scales; as well as potential changes in human-nature interactions and feedbacks under changing climate extremes and societal demands.
- Denaro, S., A. Castelletti, M. Giuliani, and G. Characklis (2020), Insurance portfolio diversification through bundling for competing agents exposed to uncorrelated drought and flood risks, Water Resources Research, 56, 1–20
- Guimarães Nobre, G., H. de Moel, M. Giuliani, K. Bischiniotis, J.C.J.H. Aerts, and P.J. Ward (2020), What will the weather do? Forecasting flood losses based on oscillation indices, Earth’s Future, 8
- Giudici, F., A. Castelletti, M. Giuliani, and H.R. Maier (2020), An active learning approach for identifying the smallest subset of informative scenarios for robust planning under deep uncertainty, Environmental Modelling & Software, 127
- Herman, J.D., J.D. Quinn, S. Steinschneider, M. Giuliani, and S. Fletcher (2020), Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty, Water Resources Research, 56
- Bertoni, F., M. Giuliani, and A. Castelletti (2020), Integrated Design of Dam Size and Operations via Reinforcement Learning, Journal of Water Resources Planning and Management, 146(4)