Articles | Volume 21, issue 12
https://doi.org/10.5194/nhess-21-3789-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-3789-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation
Dimitra M. Salmanidou
CORRESPONDING AUTHOR
Department of Statistical Science, University College London, Gower Street London WC1E 6BT, United Kingdom
Joakim Beck
Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Peter Pazak
Aon Impact Forecasting – Earthquake Model Development, London, United Kingdom
Earth Science Institute, Slovak Academy of Sciences, Bratislava, Slovakia
Serge Guillas
Department of Statistical Science, University College London, Gower Street London WC1E 6BT, United Kingdom
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- Pseudo-Probabilistic Design for High-Resolution Tsunami Simulations in the Southwestern Spanish Coast A. González et al. 10.3390/geohazards3020016
- Optimal probabilistic placement of facilities using a surrogate model for 3D tsunami simulations K. Tozato et al. 10.5194/nhess-23-1891-2023
- A neural network-based surrogate model for efficient probabilistic tsunami inundation assessment Y. Fukutani & M. Motoki 10.1016/j.coastaleng.2025.104767
- Deep Gaussian Process Emulation using Stochastic Imputation D. Ming et al. 10.1080/00401706.2022.2124311
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- Uncertainty in Manning’s roughness coefficient in multilevel simulations of future tsunamis in Sumatra K. Li et al. 10.1098/rspa.2024.0637
- Rapid prediction of rainfall-induced landslides over a wide area aided by a simulation-based surrogate model K. Tozato et al. 10.1016/j.compgeo.2025.107480
- Probabilistic Landslide Tsunami Estimation in the Makassar Strait, Indonesia, Using Statistical Emulation J. Dignan et al. 10.1029/2023EA002951
- Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates N. Ragu Ramalingam et al. 10.5194/nhess-25-1655-2025
- Embedding machine-learnt sub-grid variability improves climate model precipitation patterns D. Giles et al. 10.1038/s43247-024-01885-8
- Multi-level emulation of tsunami simulations over Cilacap, South Java, Indonesia A. Ehara et al. 10.1007/s10596-022-10183-1
13 citations as recorded by crossref.
- Rapid tsunami force prediction by mode-decomposition-based surrogate modeling K. Tozato et al. 10.5194/nhess-22-1267-2022
- Efficient probabilistic prediction of tsunami inundation considering random tsunami sources and the failure probability of seawalls Y. Fukutani et al. 10.1007/s00477-023-02379-3
- Pseudo-Probabilistic Design for High-Resolution Tsunami Simulations in the Southwestern Spanish Coast A. González et al. 10.3390/geohazards3020016
- Optimal probabilistic placement of facilities using a surrogate model for 3D tsunami simulations K. Tozato et al. 10.5194/nhess-23-1891-2023
- A neural network-based surrogate model for efficient probabilistic tsunami inundation assessment Y. Fukutani & M. Motoki 10.1016/j.coastaleng.2025.104767
- Deep Gaussian Process Emulation using Stochastic Imputation D. Ming et al. 10.1080/00401706.2022.2124311
- Probabilistic landslide-generated impulse waves estimation in mountain reservoirs, a case study H. Ma et al. 10.1007/s10064-024-04003-2
- Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES E. Baker et al. 10.5194/gmd-15-1913-2022
- Uncertainty in Manning’s roughness coefficient in multilevel simulations of future tsunamis in Sumatra K. Li et al. 10.1098/rspa.2024.0637
- Rapid prediction of rainfall-induced landslides over a wide area aided by a simulation-based surrogate model K. Tozato et al. 10.1016/j.compgeo.2025.107480
- Probabilistic Landslide Tsunami Estimation in the Makassar Strait, Indonesia, Using Statistical Emulation J. Dignan et al. 10.1029/2023EA002951
- Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates N. Ragu Ramalingam et al. 10.5194/nhess-25-1655-2025
- Embedding machine-learnt sub-grid variability improves climate model precipitation patterns D. Giles et al. 10.1038/s43247-024-01885-8
1 citations as recorded by crossref.
Latest update: 11 Aug 2025
Short summary
The potential of large-magnitude earthquakes in Cascadia poses a significant threat over a populous region of North America. We use statistical emulation to assess the probabilistic tsunami hazard from such events in the region of the city of Victoria, British Columbia. The emulators are built following a sequential design approach for information gain over the input space. To predict the hazard at coastal locations of the region, two families of potential seabed deformation are considered.
The potential of large-magnitude earthquakes in Cascadia poses a significant threat over a...
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