AI, Language Models and Disaster Risk Reduction

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I came across an interesting blog from the European Geosciences Union (EGU) by Paulo Hader. Link to blog here: https://blogs.egu.eu/divisions/nh/2024/02/05/new-era-of-ai-how-can-foundation-models-help-disaster-risk-reduction/

Paulo suggests how Foundation Large Language models (LLM) can be incorporated into approaches for disaster risk reduction.

“While traditional machine learning models are limited in dealing with climatic variables and their interactions, the difficulty of capturing extreme events with rapidly forming characteristics is made more feasible by foundation models. In this context, achieving the ability to foresee an extreme event to the extent that we can make decisions to prevent fatalities represents a significant milestone in boosting disaster risk reduction strategies“

The blog abstract states:

“In recent decades, AI-based methods have increasingly been adopted to tackle various problems in the field of natural hazards. The escalation of climate change has fuelled the complexity of tasks within the field of disaster risk reduction, such as capturing the formation of an extreme event timely to evacuate an area at risk. In this context, with the greater availability of data and computerised methods, we have had the application of mostly machine learning and deep learning methodologies as great allies for obtaining rapid responses to better understand the dynamics of natural phenomena. However, the emergence of foundation models could represent an even more significant leap forward.”