| Project Detail |
A new pathway to climate risk assessment
How big is the risk of a climate-related physical event that may cause damage or loss? While risk assessment and adaptation responses are a priority, the methods need improvement. To date, the assessment of climate hazards, risk and resilience has usually been based on static models which lose the appreciation of the temporal and spatial dynamics and complex feedback responses. The implementation of novel approaches and models is not always easy. Barriers include the availability, cost and reliability of input data sets, computational times and costs, and the lack of consideration of the combined effect of multiple hazards. With the support of the Marie Sklodowska-Curie Actions, the EXPEDITE project will test machine learning and data science techniques to remove these barriers.
Despite the urgent need for adopting multi-hazard risk approaches and for the implementation of resilience-enhancing measures in
the EU and at the global scale, several challenges for effective implementation of risk assessment and adaptation responses remain.
In particular, the assessment of climate hazards, risk and resilience is often based on static models which lose the appreciation of the
temporal and spatial dynamics and complex feedback responses within the system. As a consequence, the information provided
often fails to inform decision-makers and other end-users with the correct data to be actioned through timely responses to emerging
risks. Barriers to development and implementation of novel approaches and models include the availability, cost and reliability of
input datasets; the computational times and costs; the lack of consideration of the combined effect of multiple hazards; the spatial
and temporal changes in the exposure of assets and services; and the complex adaptive responses of government, society and the
environment to emerging risks. EXPEDITE aims at exploring new pathways to reduce and remove these barriers by exploring, testing
and deploying machine learning and data science techniques and by developing and testing a climate service prototype, tailored to
end-users. These may include institutional clients (such as Regions) the private sector or individual consumers. The research project,
which will last 24 months, will be mainly conducted at CMCC@Ca’Foscari in Venice (Italy) under the supervision of Prof. Andrea
Critto, with targeted secondments for advanced training in machine learning and data science at MALGA, University of Genoa and
for climate service design and prototyping at GECOsistema srl, a specialised R&D consulting lab. A targeted dissemination and
communication plan will allow EXPEDITE to share the research activities, outcomes and outputs with researchers, policymakers, the
private sector and the general public. |
