Document :

ITU-D SG01 RAPPORTEUR GROUP MEETING  Contribution  61

Title :

Leveraging AI to Enhance Multi-Hazard Early Warning Systems

Date :

2026-09-03

Source :

BDT Focal point for Question 3/1

AI/Question :

Q3/1

Meeting :

2026-09-28

Access :

Restricted to TIES users [ITU-D]

Abstract :

This contribution presents to the ITU Membership key findings and recommendations from the report 'Leveraging AI to Enhance Multi-Hazard Early Warning Systems (MHEWS)', an outcome of the AI for Early Warnings for All (EW4All) Group led by the ITU Telecommunication Development Bureau (BDT) in the area of AI for Multi-Hazard Early Warning Systems (MHEWS), as part of the broader global efforts under the Early Warnings for All (EW4All) initiative.
Drawing on practical cases from different regions, it highlights how AI and digital technologies can support the four pillars of MHEWS, with particular attention to disaster risk knowledge; detection, observation, monitoring and forecasting; warning dissemination and communication; and preparedness and response. The contribution also presents key considerations for the responsible, inclusive and effective use of AI to help address existing early-warning gaps and advance the goal of Early Warnings for All.