ENVIRONMENTAL INTELLIGENCE
for Global Change
ABOUT US
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.
NEWS
Prof. Andrea Castelletti, head of the eiLab, has been interviewed into L'Eco di Bergamo to recap main learning from the AMBER project (Adaptive…
The competition for water, energy, and food is intensifying, and this is happening at…
The eiLab led a study in collaboration with Technische Universität Berlin, Einstein Center Digital Future, Delft University of Technology,…
The eiLab carried out a systematic review to identify the current research trends and set the future…
The eiLab participates in a study in collaboration with colleagues from the Fondazione Centro Euro-Mediterraneo…
The eiLab led a study in collaboration with colleagues from the University of Padova and Stanford University,…
In June 21, 2023, from 9 am to 6:30 pm CEST, the META is organizing a working day on the most recent trends in research on climate change.
The…
eiTweets
Downscaling, bias correction, and spatial adjustment of extreme tropic... https://www.sciencedirect.com/science/article/pii/S2212094724000859 - new paper led by @GuidoAsc with @MxgTeo @andrea_ficchi
Just wrapped up a 4-day workshop & hands-on training on #AI for #climate services & #environmental applications at #UEM in Maputo, with @GuidoAsc @eiPolimi. With several amazing local partners, we explored the potential of #AI to boost the resilience for #cyclones & #floods
A Systematic Framework for Data Augmentation for Tropical Cyclone Intensity Estimation Using Deep Learning - new paper led by @GuidoAsc with @GiulioPalcic @eiPolimi #MachineLearning
Publications
- Foroumandi, E., Moradkhani, H., Sanchez-Vila, X., Singha, K., Castelletti, A., Destouni, G. (2023), ChatGPT in Hydrology and Earth Sciences: Opportunities, prospects and concerns, Water Resources Research, e2023WR036288
- Crippa, N., Grillakis, M.G., Tsilimigkras, A., Yang, G., Giuliani, M., Koutroulis, A.G. (2023). Seasonal forecast-informed reservoir operation. Potential benefits for a water-stressed Mediterranean basin, Climate Services, 32, 100406
- Salcedo-Sanz, S., Perez-Aracil, J., Ascenso, G., del Ser, J., Casillas-Perez, D., Kadow, C., Fister, D., Barriopedro, D., Garcia-Herrera, R., Giuliani, M., Castelletti, A. (2023). Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review, Theoretical and Applied Climatology, in press
- Carlino, A., Wildemeersch, M., Chawanda, C.J., Giuliani, M., Sterl, S., Thiery, W., van Griensven, A., Castelletti, A. (2023). Declining cost of renewables and climate change curb the need for African hydropower expansion, Science, 381, 6658
- Arnold, W., Salazar, J. Z., Carlino, A., Giuliani, M., Castelletti, A. (2023). Operations eclipse sequencing in multipurpose dam planning, Earth’s Future, 11, e2022EF003186