Automated disaster event extraction to understand lessons learned: A large-scale text analysis on the scientific literature of floods, droughts, and landslides.

A natural hazard event that highly impacted a society might trigger a wave of post-disaster research analysis, which looks into the cause of the disaster, the types of impact, or any lessons learned to prevent similar events in the future. In short, post-disaster research contains valuable knowledge that should be utilized in disaster risk management. However, in the past 70 years, the scientific community published around 600,000 articles on hydro-hazards, such as floods, droughts, and landslides. Finding articles that describe specific disaster events and synthesizing their knowledge is not humanly possible anymore due to near exponentially increasing numbers of publications. However, recent advancements in large language models allow the analysis and extraction of described disaster events in the scientific literature.

Here we make use of the Wealth over Woe scientific abstract dataset (Stein et al. 2024), with abstracts that were automatically annotated for hydro-hazards and geolocation.  It allows us to track publication trends and to identify disaster events that triggered a wave of new research. We additionally make use of the large language model Llama 70B to extract specific hazard events mentioned in each abstract (e.g. 2003 summer drought in Europe, Pakistan flood in 2010, 2002 Elbe flood, etc.) as well as other described details surrounding the event.

While we know that hydro-hazard research is biased against low-income countries, exceptional disaster events can shift research priorities for several years. The additional funding can support valuable local post-disaster research. The named event recognition can therefore help us answer questions such as: What kind of hydro-hazards are studied in detail and where? What are the key research foci for post-disaster analysis? And are there regional differences to these answers?