projects

Global early warning system on forced displacement

Completed
AI for efficient, evidence-informed humanitarianism
AI for efficient, evidence-informed humanitarianism
Start Date
Total Project Cost
USD 64,619.000
Country
Switzerland
Project Team
Division of Emergency and Programme Support (DEPS) , Violence Early-Warning System (ViEWS) , Department of Peace and Conflict Research at Uppsala University

Challenge

Currently, countries at high risk of humanitarian emergencies are identified jointly by UNHCR operations, regional bureaux and headquarters, but the agency lacks systematic, data-driven early warning systems on forced displacement that provide real-time alerts. We must boost UNHCR’s ability to forecast in order to build our capacity to act.

Solution

UNHCR’s Division of Emergency, Security and Supply will design a prototype data-driven early warning system combining different data sources, based on research of internal initiatives and external systems used by humanitarian agencies, academia and the private sector. The system will be piloted in several country operations.

Impact

Improved anticipation of and response to complex emergencies, strengthened partnerships, and a role in data-driven anticipatory humanitarianism.

Project impact

100+
early warning systems reviewed, focused on armed conflict, natural hazards, & epidemics
1
live, interactive global EWS catalogue publicly available
14
best-in-class systems shortlisted as priority partners for UNHCR's future EWS

Other information

UNHCR had no systematic view of the global early warning landscape, making it difficult to anticipate displacement triggered by conflict, natural hazards, or epidemics. Funded by the Data Innovation Fund and delivered in partnership with the ViEWS consortium (Uppsala University and PRIO), this project produced the first comprehensive global mapping of more than 100 operational early warning systems across all major hazard categories, evaluated their feasibility, reliability and predictive performance, and shortlisted the strongest candidates to feed into a future UNHCR displacement EWS. The project also delivered a clear R&D roadmap for building UNHCR's own displacement forecasting capability on top of best-in-class external models, providing a strategic foundation for anticipatory action and shifting UNHCR from reactive to predictive humanitarian response.

After this comprehensive scoping and research exercise, the global early warning system on forced displacement was fast-tracked for scale and investment through UNHCR's Innovation Accelerator.

Please find this pilot project's main output, a publicly available catalogue on EWS, here. Find a press release on the accelerated project to develop a global early warning and response system on forced displacement – a collaboration with the Luxembourg Institute of Science and Technology – here.