GENEVA – A new report released under the Early Warnings for All (EW4All) initiative highlights the growing role of artificial intelligence (AI) in strengthening multi-hazard early warning systems, demonstrating how emerging technologies can help save lives, protect livelihoods, and improve disaster preparedness.
The report, "Leveraging AI to Enhance Multi-Hazard Early Warning Systems," was produced by the International Telecommunication Union (ITU) as part of the Early Warnings for All (EW4All) initiative.
It explores how AI can address existing gaps in early warning systems while making them more effective, resilient, and inclusive.
According to the report, AI supports every stage of the early warning cycle by improving disaster risk assessment, enhancing hazard detection and forecasting, enabling more targeted and inclusive warning dissemination, and strengthening preparedness and anticipatory action.
The report notes that AI can process vast amounts of data from satellites, radar systems, sensors, and historical records to deliver faster and more accurate forecasts, while helping authorities tailor emergency alerts to different communities.
Several case studies demonstrate AI's practical applications. In Tonga, AI-generated digital twin technology was used to map critical infrastructure and simulate flooding scenarios, improving evacuation planning.
In the United States, experimental machine learning models enhanced hurricane forecasting by providing probabilistic predictions of storm intensity and track.
In Liberia, AI-powered connectivity mapping identified communities unable to receive emergency alerts due to poor mobile network coverage. Meanwhile, in Colombia, AI-supported participatory mapping helped integrate indigenous knowledge into disaster preparedness and environmental conservation.
The report also highlights China's MAZU AI Agent, an integrated platform that combines satellite data, radar observations, and localized AI models to support the entire early warning process, from risk assessment and forecasting to public alerts and emergency response guidance.
Despite AI's potential, the report stresses that technology alone is not sufficient. Effective AI-driven early warning systems require robust observation infrastructure, reliable data, strong governance, human oversight, interoperable systems, and sustained investment.
To accelerate implementation, the report recommends strengthening monitoring infrastructure, establishing governance frameworks for AI, adopting human-centered and inclusive design, promoting interoperability across early warning systems, increasing long-term investment, and prioritizing countries and communities with the greatest protection gaps.
The report comes as climate change continues to increase the frequency and intensity of extreme weather events worldwide, underscoring the need for more comprehensive and technology-driven early warning systems capable of protecting vulnerable populations.
