Strengthening disaster risk knowledge in Tonga's Tongatapu Island: Limited data on buildings, mangroves, and other critical assets have constrained risk assessment and preparedness. The use of AI-driven tools, including a pre-trained AI model to map and count critical assets, including buildings and coconut trees, has produced a scenario analysis and realistic inundation simulation to produce information on vulnerable assets, allowing authorities to significantly improve local evacuation planning.
Images above show the AI-simulated Digital Twin of Tongatapu, allowing scenarios for sea level rise and flooding within a four minute gap.
Advancing forecasting and hazard detection during Hurricane Melissa: To improve tropical cyclone track and intensity predictions, the National Hurricane Center of the United States has examined experimental machine learning (ML)–based guidance alongside conventional dynamical models. An experimental ML tropical cyclone ensemble forecasting system developed by Google generates probabilistic predictions of storm track and intensity by producing up to 50 ensemble forecast scenarios. Forecast discussions and subsequent analyses indicated that a large portion of the Google ensemble members projected that Hurricane Melissa in October 2025 could intensify to Category 5 strength, highlighting the possibility of extreme intensification and providing additional probabilistic guidance on storm evolution.
Improving warning dissemination and communication in Liberia: Under the AI for EW4All Group, ITU, in collaboration with Microsoft, IHME, and Planet Labs, developed the Early Warning Connectivity Map (EWCM) to identify connectivity coldspots where populations lack the network access needed to receive emergency notifications. Integrating a 100-metre resolution population dataset generated using AI, the tool was deployed in Liberia. By combining ITU data with cell site details provided by the Liberia Telecommunications Authority and mobile operators, the system generated precise maps to show where people were covered by cellular networks. This information was combined with maps on flood-prone areas to show where people at risk were not reachable via mobile alert systems.
Supporting preparedness and anticipatory action through Sketch Map: Despite legal recognition granting community personhood to protect Colombia's Río Atrato Basin, data sources remain scarce and fail to incorporate ancestral knowledge and traditional conservation practices. This participatory mapping methodology digitizes community data through maps that overlay with authoritative sources for integrated conservation analysis. Using the AI-embedded Sketch Map Tool, communities lead workshops where participants draw conservation project locations on paper maps, with AI automatically detecting sketches to generate digital datasets integral to consultation processes.
Enabling an integrated end-to-end approach to EWS: Some AI-based solutions apply across the entire early warning value chain. China's Multi-hazard, Alert, Zero-gap, Universal (MAZU) AI Agent for early warnings, developed by the Shanghai Meteorological Service and the China Meteorological Administration, is one such example, integrating advanced algorithms and multi-source data — including satellites, radar, and localized AI models — to support every stage of the early warning process.
Aligned with the four EW4All pillars, seen in image above, MAZU provides online access to multi-hazard risk assessment tools and databases for disaster risk knowledge; flexible monitoring and forecast analysis tools for detection and observation; AI-generated disaster condition reports for warning dissemination; and role-based, disaster-specific emergency guidelines for preparedness and response. A "three-terminal" architecture tailors this to different users — an all-in-one machine for professional departments, a tablet for sectors like ports and shipping, and a mobile app that delivers location-based alerts and evacuation guidance directly to the public.