AI to the rescue: enhance disaster early warnings with tech tools

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There are many examples of how AI is enhancing the effectiveness of early warning: by forecasting and monitoring natural hazards, assessing the robustness of infrastructure and disseminating warnings.

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Various companies released AI-based medium-range weather-forecasting models in 2023, including Google DeepMind in London, Huawei in Shenzhen, China, and Nvidia in Santa Clara, California. In terms of speed and precision, some of these models outperform conventional tools. Furthermore, AI is considered well suited to improving forecasting and monitoring of small-scale events, such as thunderstorms, which can include extreme rainfall or damaging hail and give rise to tornadoes.

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All of this work shows the promise of AI for disaster-warning systems. However, AI tools created in the absence of international standards could have a variety of problems, including data bias and not being compatible or interoperable with each other. Because disasters can move across borders, this is a lost opportunity for continuous early-warning coverage.

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When laying the groundwork for standards, it is important that stakeholders from different regions contribute to the discussion. Each country has distinct values and priorities, and the standards will need to be used across borders. Participation might also encourage stakeholders to incorporate such standards into their own national legislation.

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