An automated approach for developing geohazard inventories using news: Integrating NLP, machine learning, and mapping

Abstract. Spatiotemporal inventories of natural hazards are essential for comprehending the building of resilient societies; yet, restricted access to global inventories hinders the advancement of mitigation strategies. Consequently, we developed an approach that enhances the capability of online newspapers in the creation of natural hazard inventory by utilizing web scraping, natural language processing (NLP), clustering, and geolocation of textual data. Here, we use the online newspapers from 1997 to 2023 in Türkiye to employ our approach. In the first stage, we retrieved 15,569 news by using our tr-news-scraper tool considering wildfire, flood, landslide, and sinkhole-related natural hazard news. Further, we utilized NLP preprocessing approaches to refine the raw texts obtained from newspaper sources, which were subsequently clustered into 4 natural hazard groups resulting in 3928 news. In the final stage of the approach, we developed a method, which geolocates the news using the Open Street Map (OSM) Nominatim tool, ending up with a total of 13940 natural hazard incidents derived from news comprising multiple incidents across various locations. As a result, we mapped 9609 floods, 1834 wildfires, 1843 landslides, and 654 sinkhole formation incidents from online newspaper sources, showing spatiotemporally consistent distribution with existing literature. Consequently, we illustrated the potential of online newspaper articles in the development of natural hazard inventories with our approach from the web sources as text data to map by leveraging the capabilities of web scraping, NLP, and mapping techniques.