Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2421-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2421-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jul 2025
Research article |
| 21 Jul 2025
| 21 Jul 2025
An automated approach for developing geohazard inventories using news: integrating natural language processing (NLP), machine learning, and mapping
BRGM, 3 avenue Claude Guillemin, Orléans, 45060, France
Eurasia Institute of Earth Sciences, Istanbul Technical University, İstanbul, Türkiye
Invited contribution by Aydogan Avcioglu, recipient of the EGU Soil System Sciences Outstanding Student and PhD candidate Presentation Award 2022.
Ogün Demir
Nezahat Gökyiğit Botanic Garden, Biodiversity Information Department, İstanbul, Türkiye
Tolga Görüm
Eurasia Institute of Earth Sciences, Istanbul Technical University, İstanbul, Türkiye
Related authors
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Hunter N. Jimenez, Erkan Istanbulluoglu, Tolga Gorum, Thomas A. Stanley, Pukar M. Amatya, Hakan Tanyas, Mehmet C. Demirel, Aykut Akgun, and Deniz Bozkurt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3011, https://doi.org/10.5194/egusphere-2025-3011, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
After a major earthquake struck near the Türkiye/Syria border in February 2023, a powerful storm brought intense rainfall to the region, triggering additional landslides. We used satellite data and a physics-based model to map probabilistic landslide hazard using both coseismic and hydrologic drivers. We also explored how the sequence of these disasters affected landslide risk. Finally, we offer a method for seasonal forecasting of landslide hazard in at-risk areas using the historic climate.
S. Coskun, C. Bayik, S. Abdikan, T. Gorum, and F. Balik Sanli
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 497–502, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-497-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-497-2023, 2023
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Short summary
We demonstrate an approach for the development of inventories from internet sources to geolocalize geohazard incidents. We created a tool that autonomously gets news, processes it using natural language processing, and then builds inventories. Consequently, we present spatiotemporal inventories for geohazards, resulting in a total of 13 940 incidents between 1997 and 2023 in Türkiye. Our alternative and easy-to-implement development inventory method aids geohazard management and resilience.
We demonstrate an approach for the development of inventories from internet sources to...
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