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dc.contributor.author
Chmiel, Malgorzata
dc.contributor.author
Walter, Fabian
dc.contributor.author
Wenner, Michaela
dc.contributor.author
Zhang, Zhen
dc.contributor.author
McArdell, Brian W.
dc.contributor.author
Hibert, Clement
dc.date.accessioned
2021-03-02T07:26:41Z
dc.date.available
2021-03-02T03:53:45Z
dc.date.available
2021-03-02T07:26:41Z
dc.date.issued
2021
dc.identifier.issn
0094-8276
dc.identifier.issn
1944-8007
dc.identifier.other
10.1029/2020GL090874
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/472319
dc.identifier.doi
10.3929/ethz-b-000472319
dc.description.abstract
Automatic identification of debris flow signals in continuous seismic records remains a challenge. To tackle this problem, we use machine learning, which can be applied to continuous real-time data. We show that a machine learning model based on the random forest algorithm recognizes different stages of debris flow formation and propagation at the Illgraben torrent, Switzerland, with an accuracy exceeding 90 %. In contrast to typical debris flow detection requiring instrumentation installed in the torrent, our approach provides a significant gain in warning times of tens of minutes to hours. For real-time data from 2020, our detector raises alarms for all 13 independently confirmed Illgraben events, giving no false alarms. We suggest that our seismic machine-learning detector is a critical step toward the next generation of debris-flow warning, which increases warning times using simpler instrumentation compared to existing operational systems. © 2020. The Authors.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
American Geophysical Union
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.title
Machine Learning Improves Debris Flow Warning
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
dc.date.published
2020-12-29
ethz.journal.title
Geophysical Research Letters
ethz.journal.volume
48
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
Geophys. Res. Lett.
ethz.pages.start
e2020GL090874
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Glacial Hazard Monitoring with Seismology (GlaHMSeis)
en_US
ethz.identifier.scopus
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.grant.agreementno
157551
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
SNF-Förderungsprofessuren Stufe 2
ethz.date.deposited
2021-03-02T03:53:55Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-02T07:26:53Z
ethz.rosetta.lastUpdated
2021-03-02T07:26:53Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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