Big Data for natural hazards: lessons from and for environments with low technical capacities

A small grant from Public Policy was provided to support this work by our UCL community to enhance policy engagement and impact.

4 February 2020

Project information

Grant: Engagement Award
Awarded amount: £1,500
Awardees: Michael Veale, UCL Department of Science, technology, Engineering and Public Policy and Pablo Suarez, Innovation at the Red Cross/Red Crescent Climate Centre

Problem: There is limited understanding of how big data and new technologies can improve our ability to detect emerging natural hazards (including human, animal and plant diseases) around the world.

Project: Exploring the rapid expansion of data-driven machine learning models in the public sector in low data, low technical capacity zones through interviews with key practitioners and policymakers. A workshop with key decisionmakers to highlight the challenges and opportunities of public sector machine learning in different contexts.

Policy audience: The Red Cross; Department for International Development; Government Office for Science; Government of Togo.

Impacts and Outputs

Using the original UCL PP award the project has successfully leveraged around $30,000 of extra funding, mostly from the World Bank Global Facility for Disaster Reduction and Recovery, in order to hold a workshop in Addis Ababa in May, linked to the World Hydropower Congress.

The workshop brought together stakeholders from different African governments and intergovernmental organisations, as well as dam owners and analysts, to explore a number of papers and primers on machine learning for humanitarian risk are being developed.

Find out more about Engagement awards →

Contact us

If you have any questions please email public-policy@ucl.ac.uk.

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