Deep learning can predict global earthquake-triggered landslides
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F25%3A00637064" target="_blank" >RIV/67985530:_____/25:00637064 - isvavai.cz</a>
Result on the web
<a href="https://academic.oup.com/nsr/article/12/7/nwaf179/8128033?login=false" target="_blank" >https://academic.oup.com/nsr/article/12/7/nwaf179/8128033?login=false</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/nsr/nwaf179" target="_blank" >10.1093/nsr/nwaf179</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning can predict global earthquake-triggered landslides
Original language description
Earthquake-triggered (coseismic) landsliding is among the most lethal of disasters, and rapid response is crucial to prevent cascading hazards that further threaten lives and infrastructure. Current prediction approaches are limited by oversimplified physical models, regionally focused databases, and retrospective statistical methods, which impede timely and accurate hazard assessments. To overcome these constraints, we developed the first comprehensive global database of similar to 400 000 landslides associated with 38 of the most catastrophic earthquakes over the past 50 years. Leveraging this extensive dataset, we developed advanced deep-learning models that predict the probability of landsliding for any earthquake worldwide with an average spatial accuracy of similar to 82% in less than a minute, without relying on prior local knowledge. Our framework enables swift disaster evaluation during the critical early hours following an earthquake while also enhancing pre-event hazard planning. This study offers a scalable and efficient tool to mitigate the catastrophic impacts of earthquake-triggered landslides, representing a transformative advance in global geohazard prediction.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10505 - Geology
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
National Science Review
ISSN
2095-5138
e-ISSN
2053-714X
Volume of the periodical
12
Issue of the periodical within the volume
7
Country of publishing house
CN - CHINA
Number of pages
12
Pages from-to
nwaf179
UT code for WoS article
001507546100001
EID of the result in the Scopus database
2-s2.0-105008450479