Space–time landslide hazard modeling via Ensemble Neural Networks Article Swipe
Ashok Dahal
,
Hakan Tanyaş
,
C.J. van Westen
,
M. van der Meijde
,
P. Martín
,
Raphaël Huser
,
Luigi Lombardo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/nhess-24-823-2024
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/nhess-24-823-2024
Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physically based models. The part of the geoscientific community in developing data-driven models has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimate when landslides may occur via models that belong to the early-warning system or to the rainfall-threshold classes. In this context, few published research works have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size (i.e., areas or volumes), has hardly ever been modeled over space and time. However, technological advancements in data-driven models have reached a level of maturity that allows all three components to be modeled (Location, Frequency, and Size). This work takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this, we used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 km × 1 km and classified or regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6 month resolution. The results are promising as our model performs satisfactorily both in the susceptibility (AUC = 0.93) and density prediction (Pearson r = 0.93) tasks over the entire spatio-temporal domain. This model takes a significant distance from the common landslide susceptibility modeling literature, proposing an integrated framework for hazard modeling in a data-driven context.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/nhess-24-823-2024
- https://nhess.copernicus.org/articles/24/823/2024/nhess-24-823-2024.pdf
- OA Status
- gold
- Cited By
- 27
- References
- 111
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392776910
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392776910Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/nhess-24-823-2024Digital Object Identifier
- Title
-
Space–time landslide hazard modeling via Ensemble Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-08Full publication date if available
- Authors
-
Ashok Dahal, Hakan Tanyaş, C.J. van Westen, M. van der Meijde, P. Martín, Raphaël Huser, Luigi LombardoList of authors in order
- Landing page
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https://doi.org/10.5194/nhess-24-823-2024Publisher landing page
- PDF URL
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https://nhess.copernicus.org/articles/24/823/2024/nhess-24-823-2024.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://nhess.copernicus.org/articles/24/823/2024/nhess-24-823-2024.pdfDirect OA link when available
- Concepts
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Landslide, Hazard, Context (archaeology), Seismology, Geology, Artificial neural network, Computer science, Warning system, Early warning system, Cartography, Data mining, Geography, Artificial intelligence, Telecommunications, Chemistry, Organic chemistry, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
27Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 11Per-year citation counts (last 5 years)
- References (count)
-
111Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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