The eiLab contributed to a systematic review assessing how machine learning has been used in drought research over the past two decades. Published in Water Resources Research, the review analyses 544 scientific papers on machine learning for drought science, focusing not only on which methods are used, but also on
We are pleased to share a new publication developed in collaboration with colleagues from the University of Cambridge, the Environment Agency, and several UK partners. This perspective explores the potential of artificial intelligence (AI) to support water quality management and regulation, addressing increasingly complex challenges driven by climate change, pollution,
The eiLab contributed to a study developing an explainable machine learning model to estimate intense Mediterranean cyclone activity from large-scale climate variables. In the study, published in Journal of Geophysical Research: Machine Learning and Computation, the authors developed a convolutional neural network to estimate monthly Mediterranean cyclone activity, measured through
We are pleased to share a new publication resulting from a collaboration between our lab and colleagues from Cornell University and University of California Davis. In this study, we develop a novel framework to support adaptive reservoir operations under climate change, addressing a key challenge in water resources management: when
Prof. Andrea Castelletti, head of the eiLab, has been interviewed into L’Eco di Bergamo to recap main learning from the AMBER project (Adaptive Management of Barriers in European Rivers), founded in 2016 by the European Union’s Horizon 2020 research and innovation programme with the aim to deliver innovate solutions to
The eiLab led a study in collaboration with Technische Universität Berlin, Einstein Center Digital Future, Delft University of Technology, University of Melbourne, TU Dortmund University, Universitat Politècnica de Catalunya – BarcelonaTECH, and University of Seville, to carried out a systematic review on Model Predictive Control (MPC) and its recently gained increasing interest in
The eiLab carried out a systematic review to identify the current research trends and set the future research agenda on climate change awareness, perceived impacts and adaptive capacity from farmers’ experiences and behavior. Individuals and communities socially construct risk, and societies with greater risk perception may be more apt to
The eiLab participates in a study in collaboration with colleagues from the Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, to address the questions of whether Genesis Potential Indices (GPIs) are still relevant in the era of Tropical Cyclones-permitting climate model ensembles, and whether they have sufficient predictive skills. Tropical Cyclones (TC) are extreme
The eiLab led a study in collaboration with colleagues from the University of Padova and Stanford University, to develop a sediment routing model (D-CASCADE) able to assess the impacts of reservoirs and their management strategies on river sediment connectivity. Trade-offs between hydropower generation and sediment connectivity across cascades of multiple
The eiLab led a study in collaboration with colleagues from the University of Padova and Macquarie University, for the development of a new dynamic sediment (dis)connectivity model, and its application for river basin study and management. The result is the basin-scale, dynamic sediment connectivity model D-CASCADE, which combines concepts of