Speech-recognition in landslide predictive modelling: A case for a next generation early warning system Article Swipe
Zhice Fang
,
Hakan Tanyaş
,
Tolga Görüm
,
Ashok Dahal
,
Yi Wang
,
Luigi Lombardo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.envsoft.2023.105833
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.envsoft.2023.105833
Traditional landslide early warnings are based on the notion that intensity-duration relations can be approximated to single precipitation values cumulated over fixed time windows. Here, we take on a similar task being inspired by modeling architectures typical of speech-recognition tasks. We aim at classifying the Turkish landscape into 5 km grids assigned with a landslide susceptibility estimate. We collected all available national information on precipitation-induced landslide occurrences. This information is passed to a Long Short-Term Memory equipped with the whole rainfall time series, obtained from daily CHIRPS data. We test this model randomizing the presence/absence data to represent the slope instability over Turkey and over 13 years under consideration (2008–2020) and assessing different time windows. Results show that the inclusion of the full precipitation signal rather than its scalar approximation leads to a substantial increase in prediction power (approximately 20%). This may potentially pave the road for a new generation of speech-recognition-based landslide early warning systems.
Related Topics
Concepts
Landslide
Warning system
Computer science
Early warning system
Turkish
Duration (music)
Long short term memory
Precipitation
Speech recognition
SIGNAL (programming language)
Time series
Predictive power
Term (time)
Artificial intelligence
Machine learning
Meteorology
Geology
Telecommunications
Geography
Seismology
Recurrent neural network
Acoustics
Artificial neural network
Quantum mechanics
Physics
Philosophy
Epistemology
Programming language
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.envsoft.2023.105833
- OA Status
- hybrid
- Cited By
- 27
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386969082
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386969082Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.envsoft.2023.105833Digital Object Identifier
- Title
-
Speech-recognition in landslide predictive modelling: A case for a next generation early warning systemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-22Full publication date if available
- Authors
-
Zhice Fang, Hakan Tanyaş, Tolga Görüm, Ashok Dahal, Yi Wang, Luigi LombardoList of authors in order
- Landing page
-
https://doi.org/10.1016/j.envsoft.2023.105833Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.envsoft.2023.105833Direct OA link when available
- Concepts
-
Landslide, Warning system, Computer science, Early warning system, Turkish, Duration (music), Long short term memory, Precipitation, Speech recognition, SIGNAL (programming language), Time series, Predictive power, Term (time), Artificial intelligence, Machine learning, Meteorology, Geology, Telecommunications, Geography, Seismology, Recurrent neural network, Acoustics, Artificial neural network, Quantum mechanics, Physics, Philosophy, Epistemology, Programming language, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
27Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 11, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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