IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i20p9110-d1503162.html
   My bibliography  Save this article

Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models

Author

Listed:
  • Paul Kent

    (Department of Computer Science, University of Exeter, Exeter EX1 2LU, UK)

  • Soroush Abolfathi

    (School of Engineering, The University of Warwick, Coventry CV4 7AL, UK)

  • Hannah Al Ali

    (Faculty of Mathematics and Data Science, Emirates Aviation University, Dubai P.O. Box 53044, United Arab Emirates)

  • Tabassom Sedighi

    (International Policing and Public Protection Research Institute (IPPPRI), Anglia Ruskin University, Cambridge CB1 1PT, UK)

  • Omid Chatrabgoun

    (School of Computing, Mathematics and Data Science, Coventry University, Coventry CV1 5FB, UK)

  • Alireza Daneshkhah

    (Faculty of Mathematics and Data Science, Emirates Aviation University, Dubai P.O. Box 53044, United Arab Emirates)

Abstract

This paper presents a novel mathematical framework for assessing and predicting the resilience of critical coastal infrastructures against wave overtopping hazards and extreme climatic events. A probabilistic sensitivity analysis model is developed to evaluate the relative influence of hydrodynamic, geomorphological, and structural factors contributing to wave overtopping dynamics. Additionally, a stochastic Gaussian process (GP) model is introduced to predict the mean overtopping discharge from coastal defences. Both the sensitivity analysis and the predictive models are validated using a large homogeneous dataset comprising 163 laboratory and field-scale tests. Statistical evaluations demonstrate the superior performance of the GPs in identifying key parameters driving wave overtopping and predicting mean discharge rates, outperforming existing regression-based formulae. The proposed model offers a robust predictive tool for assessing the performance of critical coastal protection infrastructures under various climate scenarios.

Suggested Citation

  • Paul Kent & Soroush Abolfathi & Hannah Al Ali & Tabassom Sedighi & Omid Chatrabgoun & Alireza Daneshkhah, 2024. "Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models," Sustainability, MDPI, vol. 16(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9110-:d:1503162
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/20/9110/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/20/9110/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
    2. Daneshkhah, Alireza & Bedford, Tim, 2013. "Probabilistic sensitivity analysis of system availability using Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 82-93.
    3. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    4. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Al Ali, Hannah & Daneshkhah, Alireza & Boutayeb, Abdesslam & Malunguza, Noble Jahalamajaha & Mukandavire, Zindoga, 2022. "Exploring dynamical properties of a Type 1 diabetes model using sensitivity approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 324-342.
    2. Matieyendou Lamboni, 2024. "Optimal Estimators of Cross-Partial Derivatives and Surrogates of Functions," Stats, MDPI, vol. 7(3), pages 1-22, July.
    3. Daniel W. Gladish & Daniel E. Pagendam & Luk J. M. Peeters & Petra M. Kuhnert & Jai Vaze, 2018. "Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 39-62, March.
    4. Zitrou, A. & Bedford, T. & Daneshkhah, A., 2013. "Robustness of maintenance decisions: Uncertainty modelling and value of information," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 60-71.
    5. Nagel, Joseph B. & Rieckermann, Jörg & Sudret, Bruno, 2020. "Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    6. Daneshkhah, A. & Stocks, N.G. & Jeffrey, P., 2017. "Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 33-45.
    7. Kapusuzoglu, Berkcan & Mahadevan, Sankaran, 2021. "Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Daneshkhah, Alireza & Bedford, Tim, 2013. "Probabilistic sensitivity analysis of system availability using Gaussian processes," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 82-93.
    9. Arnst, M. & Goyal, K., 2017. "Sensitivity analysis of parametric uncertainties and modeling errors in computational-mechanics models by using a generalized probabilistic modeling approach," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 394-405.
    10. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    11. Matieyendou Lamboni, 2020. "Uncertainty quantification: a minimum variance unbiased (joint) estimator of the non-normalized Sobol’ indices," Statistical Papers, Springer, vol. 61(5), pages 1939-1970, October.
    12. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    13. David Breitenmoser & Francesco Cerutti & Gernot Butterweck & Malgorzata Magdalena Kasprzak & Sabine Mayer, 2023. "Emulator-based Bayesian inference on non-proportional scintillation models by compton-edge probing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    14. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    15. Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
    16. Marc Kennedy & Clive Anderson & Anthony O'Hagan & Mark Lomas & Ian Woodward & John Paul Gosling & Andreas Heinemeyer, 2008. "Quantifying uncertainty in the biospheric carbon flux for England and Wales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 109-135, January.
    17. Deman, G. & Konakli, K. & Sudret, B. & Kerrou, J. & Perrochet, P. & Benabderrahmane, H., 2016. "Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 156-169.
    18. Kennedy, Marc C. & Anderson, Clive W. & Conti, Stefano & O’Hagan, Anthony, 2006. "Case studies in Gaussian process modelling of computer codes," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1301-1309.
    19. Pronzato, Luc, 2019. "Sensitivity analysis via Karhunen–Loève expansion of a random field model: Estimation of Sobol’ indices and experimental design," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 93-109.
    20. Antony M. Overstall & David C. Woods, 2016. "Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 483-505, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9110-:d:1503162. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.