A CMCC researcher brought OFIDIAPlus to a King’s College London audience, as part of a wider dialogue on AI-driven fire risk prediction across Europe
On 24 June 2026, OFIDIAPlus reached an international stage during London Climate Action Week, featured in a session titled “Wildfire predictions and risk management in a changing climate,” hosted at King’s College London (Strand Campus) and broadcast online. London Climate Action Week is among the world’s largest climate gatherings, uniting scientists, policymakers, and civil society around climate action. Further details on the session are available on the CMCC website.
The event was jointly organized by the CMCC Foundation and the Leverhulme Centre for Wildfires, Environment and Society — a joint initiative of Imperial College London, King’s College London, the University of Reading, and Royal Holloway, University of London. It brought together scientists, policymakers, and practitioners to examine how emerging technologies are transforming wildfire risk understanding and management in a warming climate.
A pilot case study in machine learning CMCC researcher Shahbaz Nihal Ahmed Alvi took part in the session’s segment on AI and machine learning for fire-risk prediction, delivering a talk titled “Machine Learning for wildfire forecasting.” Alongside another CMCC-led initiative, Alvi referenced OFIDIAPlus, drawing on its pilot sites in Puglia, Calabria, Basilicata, and Epirus to illustrate how machine learning is being deployed operationally to forecast wildfire behavior in practice.
The presentation fit into a broader programme spanning “observation to prediction” and “impacts to action,” with topics ranging from fire-carbon feedback loops to Arctic fire trends and landscape governance, featuring speakers from CMCC, Imperial College London, King’s College London, IIASA, Tropenbos International, and Carbon Brief.
Why does this matter? Featuring OFIDIAPlus’s pilot sites as a real-world example reinforces the project’s relevance to the wider European conversation on wildfire risk under climate change, and confirms its place within cutting-edge AI/ML research now considered central to fire risk management and adaptation policy. Within OFIDIAPlus, this line of work will feed into operational fire danger index maps, made available through the project’s Decision Support System.
