Towards physics-informed neural networks for landslide prediction Article Swipe
Ashok Dahal
,
Luigi Lombardo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.06785
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.06785
For decades, solutions to regional scale landslide prediction have mostly relied on data-driven models, by definition, disconnected from the physics of the failure mechanism. The success and spread of such tools came from the ability to exploit proxy variables rather than explicit geotechnical ones, as the latter are prohibitive to acquire over broad landscapes. Our work implements a Physics Informed Neural Network (PINN) approach, thereby adding to a standard data-driven architecture, an intermediate constraint to solve for the permanent deformation typical of Newmark slope stability methods. This translates into a neural network tasked with explicitly retrieving geotechnical parameters from common proxy variables and then minimize a loss function with respect to the available coseismic landside inventory. The results are very promising, because our model not only produces excellent predictive performance in the form of standard susceptibility output, but in the process, also generates maps of the expected geotechnical properties at a regional scale. Such architecture is therefore framed to tackle coseismic landslide prediction, something that, if confirmed in other studies, could open up towards PINN-based near-real-time predictions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.06785
- https://arxiv.org/pdf/2407.06785
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400518958
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400518958Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.06785Digital Object Identifier
- Title
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Towards physics-informed neural networks for landslide predictionWork title
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preprintOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-09Full publication date if available
- Authors
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Ashok Dahal, Luigi LombardoList of authors in order
- Landing page
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https://arxiv.org/abs/2407.06785Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2407.06785Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.06785Direct OA link when available
- Concepts
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Landslide, Artificial neural network, Deep neural networks, Geology, Computer science, Geography, Artificial intelligence, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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