Artificial Intelligence for Disaster Risk Reduction: A Masterclass Series by UNDRR



Disaster Risk Reduction (DRR) in Africa faces several interconnected challenges that hinder the continent's ability to prepare for, respond to, and recover from disasters effectively. These challenges are rooted in socioeconomic, environmental, political, and institutional factors.

The lack of early warning systems (EWS) and data is a critical challenge in disaster risk reduction (DRR) in Africa, as it directly affects the ability to anticipate, prepare for, and respond to disasters. For example, warnings are sometimes issued too late or in a technical or vague format that communities cannot interpret or act upon quickly, while standard protocols for issuing, escalating, and responding to warnings are lacking. 

At the same time, many countries in Africa have underdeveloped or outdated weather observation networks, with few weather stations or functioning satellite systems. As a result, real-time monitoring of rainfall, temperature, wind, and other variables is often incomplete or inaccurate.

Artificial intelligence (AI) offers tremendous potential for accelerating the implementation of the 2015-2030 Sendai Framework for Disaster Risk Reduction (DRR). On this background, the United Nations Office for Disaster Risk Reduction (UNDRR) Global Education and Training Institute (GETI), in collaboration with a range of partners, is organising a series of four masterclasses on Zoom on how AI can be applied in various aspects of DRR.

The first session will provide a general introduction to AI, while subsequent sessions will cover topics such as AI for multi-hazard early warning, urban resilience, and risk assessment, as well as principles and ethical considerations. 

The primary audience consists of civil servants implementing DRR around the world.

Date: 09 Sep, 23 Sep, 14 Oct, 21 Oct (4 Sessions) Time: 14:00-15:00 hrs. Central European Time (CET)

More information, including registration here: https://www.undrr.org/event/drr-ai-masterclass

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