Unravelling information on impactful geo-hydrological hazard events with HazMiner, a multilingual text mining method developed through a global scale coverage application

The incidence and impacts of geo-hydrological hazards such as floods, flash floods and landslides are changing globally due to anthropogenic environmental changes and increased exposure driven by population growth. Reliable datasets on geo-hydrological hazards are essential to deepen our understanding of these hazards and their impacts. However, existing geo-hydrological hazard datasets contain data gaps leading to biased interpretations, especially in low- and lower-middle-income countries where populations are commonly the most impacted. Text mining offers new opportunities in documenting geo-hydrological hazards by automatically extracting information from large text corpora. Despite its potential, current methodologies are not adapted to improve documentation in data-scarce contexts. We introduce HazMiner, a paragraph-based text mining method designed to document the location, timing and impact of geo-hydrological hazards through transformer-based natural language processing models across multiple languages and at various scales. Applied here globally on 6 366 905 news articles published from 2017 through 2024 in 58 languages, HazMiner extracted 15 313 flood, 6836 landslide and 2258 flash flood events with a known location and time information and, in some cases, impact data. Compared to existing hazard datasets, HazMiner significantly improved hazard documentation, reducing the data gaps in many regions, especially in low- and lower-middle-income countries. This new versatile multilingual method and its dataset advance the accessibility and use of text mined natural hazard data.