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,
Sea-level rise is increasing coastal flood risk, with storm surges playing a critical yet highly uncertain role. While physics-based hydrodynamic models remain the reference for simulating these processes, their computational cost limits their use for large ensembles and long term scenario analysis. In the study published in Earth’s Future, the
The eiLab carried out a novel study to identify how drought vulnerability is shifting across Europe and to develop more effective diagnostic tools for climate risk management. Drought is a complex hazard, yet conventional indices often fail to capture its actual socio-economic and ecological consequences. Since different regions respond differently
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