Articles | Volume 12, issue 6
https://doi.org/10.5194/nhess-12-2003-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-12-2003-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
25 Jun 2012
Research article |
| 25 Jun 2012
Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification
A. Sarri
Institute for Risk and Disaster Reduction, University College London, UK
S. Guillas
Institute for Risk and Disaster Reduction, University College London, UK
Department of Statistical Science, University College London, UK
F. Dias
School of Mathematical Sciences, University College Dublin, Ireland
Viewed
Total article views: 4,441 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,705 | 1,597 | 139 | 4,441 | 198 | 183 |
- HTML: 2,705
- PDF: 1,597
- XML: 139
- Total: 4,441
- BibTeX: 198
- EndNote: 183
Cited
41 citations as recorded by crossref.
- New computational methods in tsunami science J. Behrens & F. Dias https://doi.org/10.1098/rsta.2014.0382
- Probabilistic tsunami hazard assessment with simulation-based response surfaces T. Kotani et al. https://doi.org/10.1016/j.coastaleng.2020.103719
- Multifidelity Sparse-Grid-Based Uncertainty Quantification for the Hokkaido Nansei-oki Tsunami J. de Baar & S. Roberts https://doi.org/10.1007/s00024-017-1606-y
- Probabilistic Landslide Tsunami Estimation in the Makassar Strait, Indonesia, Using Statistical Emulation J. Dignan et al. https://doi.org/10.1029/2023EA002951
- Probabilistic Landslide-Generated Tsunamis in the Indus Canyon, NW Indian Ocean, Using Statistical Emulation D. Salmanidou et al. https://doi.org/10.1007/s00024-019-02187-3
- Functional emulation of high resolution tsunami modelling over Cascadia S. Guillas et al. https://doi.org/10.1214/18-AOAS1142
- Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model J. Donnelly et al. https://doi.org/10.1016/j.watres.2022.119100
- Modeling spatial dependencies of natural hazards in coastal regions: a nonstationary approach with barriers S. Chaudhuri et al. https://doi.org/10.1007/s00477-023-02519-9
- A parallel machine learning-based approach for tsunami waves forecasting using regression trees E. Cesario et al. https://doi.org/10.1016/j.comcom.2024.07.016
- Uncertainty quantification and inference of Manning’s friction coefficients using DART buoy data during the Tōhoku tsunami I. Sraj et al. https://doi.org/10.1016/j.ocemod.2014.09.001
- Uncertainties in the 2004 Sumatra–Andaman source through nonlinear stochastic inversion of tsunami waves D. Gopinathan et al. https://doi.org/10.1098/rspa.2017.0353
- Rapid tsunami force prediction by mode-decomposition-based surrogate modeling K. Tozato et al. https://doi.org/10.5194/nhess-22-1267-2022
- Numerical simulation and statistical emulation of landslide Tsunamis in the Western Makran, SE Iran A. Rashidi et al. https://doi.org/10.1007/s11001-026-09618-6
- Probabilistic, high-resolution tsunami predictions in northern Cascadia by exploiting sequential design for efficient emulation D. Salmanidou et al. https://doi.org/10.5194/nhess-21-3789-2021
- Performance Assessment of the Cloud for Prototypical Instant Computing Approaches in Geoscientific Hazard Simulations J. Behrens et al. https://doi.org/10.3389/feart.2022.762768
- On the Modelling of Tsunami Generation and Tsunami Inundation F. Dias et al. https://doi.org/10.1016/j.piutam.2014.01.029
- Discriminating the occurrence of inundation in tsunami early warning with one-dimensional convolutional neural networks J. Núñez et al. https://doi.org/10.1038/s41598-022-13788-9
- A review of approaches for submarine landslide-tsunami hazard identification and assessment J. Roger et al. https://doi.org/10.1016/j.marpetgeo.2024.106729
- Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic D. Salmanidou et al. https://doi.org/10.1098/rspa.2017.0026
- Testing Tsunami Inundation Maps for Evacuation Planning in Italy R. Tonini et al. https://doi.org/10.3389/feart.2021.628061
- Gaussian process emulation of particle method for estimating free-surface heights Y. MIZUNO & S. KOSHIZUKA https://doi.org/10.1299/jfst.2020jfst0021
- Multi-level emulation of a volcanic ash transport and dispersion model to quantify sensitivity to uncertain parameters N. Harvey et al. https://doi.org/10.5194/nhess-18-41-2018
- Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates N. Ragu Ramalingam et al. https://doi.org/10.5194/nhess-25-1655-2025
- Generating Random Earthquake Events for Probabilistic Tsunami Hazard Assessment R. LeVeque et al. https://doi.org/10.1007/s00024-016-1357-1
- CONSTRUCTION OF A TSUNAMI WATER LEVEL ESTIMATION MODEL BASED ON FAULT PARAMETERS USING DEEP LEARNING F. ISHIHARA et al. https://doi.org/10.2208/jscejj.25-17001
- Quantifying uncertainties in fault slip distribution during the Tōhoku tsunami using polynomial chaos I. Sraj et al. https://doi.org/10.1007/s10236-017-1105-9
- APPLICATION OF GAUSSIAN PROCESS REGRESSION TO IMPROVE THE EFFICIENCY OF TSUNAMI PREDICTIONS IN PROBABILISTIC TSUNAMI HAZARD ANALYSIS T. KURITA et al. https://doi.org/10.2208/jscejj.25-17193
- Statistical analysis of three-dimensional run-up heights using Gaussian process emulator of particle method Y. Mizuno et al. https://doi.org/10.1007/s40571-021-00426-w
- Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate L. Giraldi et al. https://doi.org/10.1007/s10596-017-9646-z
- Machine Learning for Tsunami Waves Forecasting Using Regression Trees E. Cesario et al. https://doi.org/10.1016/j.bdr.2024.100452
- Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation L. Marshall et al. https://doi.org/10.1029/2018JD028675
- Physics-based simulations of multiple natural hazards for risk-sensitive planning and decision making in expanding urban regions L. Jenkins et al. https://doi.org/10.1016/j.ijdrr.2022.103338
- Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model J. Beck & S. Guillas https://doi.org/10.1137/140989613
- Probabilistic quantification of tsunami current hazard using statistical emulation D. Gopinathan et al. https://doi.org/10.1098/rspa.2021.0180
- The hydrodynamics of landslide tsunamis: current analytical models and future research directions E. Renzi & P. Sammarco https://doi.org/10.1007/s10346-016-0680-z
- Efficient probabilistic prediction of tsunami inundation considering random tsunami sources and the failure probability of seawalls Y. Fukutani et al. https://doi.org/10.1007/s00477-023-02379-3
- Rheological considerations for the modelling of submarine sliding at Rockall Bank, NE Atlantic Ocean D. Salmanidou et al. https://doi.org/10.1063/1.5009552
- Can small islands protect nearby coasts from tsunamis? An active experimental design approach T. Stefanakis et al. https://doi.org/10.1098/rspa.2014.0575
- Prediction of wind loading on masked angle members in lattice tower structures A. Hadane et al. https://doi.org/10.1016/j.euromechflu.2024.10.008
- Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps J. Behrens et al. https://doi.org/10.3389/feart.2021.628772
- Bayesian calibration of the constants of thek–εturbulence model for a CFD model of street canyon flow S. Guillas et al. https://doi.org/10.1016/j.cma.2014.06.008
Latest update: 07 Jul 2026
