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EXPloring opportunitiEs for developing a risk and resilience climate service baseD on bIg daTa and machinE learning

Project description

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 Skłodowska-Curie Actions, the EXPEDITE project will test machine learning and data science techniques to remove these barriers.

Objective

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.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2021-PF-01

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Coordinator

UNIVERSITA CA' FOSCARI VENEZIA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 188 590,08
Address
DORSODURO 3246
30123 VENEZIA
Italy

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Region
Nord-Est Veneto Venezia
Activity type
Higher or Secondary Education Establishments
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Total cost

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No data

Partners (3)

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