News

Read on to explore seven diverse examples of how AI can reduce disaster risks and build resilient communities - from harnessing satellite data to predict natural hazards, to improving multilingual weather alerts and streamlining evacuation procedures.

One week after the storm, communities are coming to terms with the full extent of Hurricane Beryl’s destruction. And hurricane season is far from over.

Two related but often confused topics play into a system architecture that mitigates against failure: high availability (HA) and disaster recovery (DR).

Madagascar’s location and socio-economic conditions make it especially exposed to a wide range of interconnected hazards.

Urban Institute analyzed data from the US Census Bureau and other sources and reflected on evidence from past disasters and identified four key issues that may need to be addressed as part of an equitable recovery.

When disaster strikes, coming together to support impacted communities not only helps with their recovery – it can set them on the path to a more resilient, sustainable future.

The International Recovery Forum 2024 concluded successfully in Kobe, Japan, marking a big step forward in global efforts towards resilient recovery from disasters.

Climate change is increasing the frequency and intensity of extreme weather events in the Caribbean, and for small islands such as Dominica (not to be confused with the much larger Dominican Republic) it is an existential threat.

On 8 November 2013 Typhoon Haiyan made landfall in the Philippines. Ten years on, survivors are still rebuilding their lives, but wide-ranging resilience measures mean that previously at-risk communities are now better protected.

The social and economic consequences of disasters are a reminder of our shared vulnerability and a reason to initiate a global movement to both mitigate and adapt to climate change.