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Volume 16
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Article

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

by
Paul Kent
1,
Soroush Abolfathi
2,
Hannah Al Ali
3,
Tabassom Sedighi
4,
Omid Chatrabgoun
5 and
Alireza Daneshkhah
3,*
1
Department of Computer Science, University of Exeter, Exeter EX1 2LU, UK
2
School of Engineering, The University of Warwick, Coventry CV4 7AL, UK
3
Faculty of Mathematics and Data Science, Emirates Aviation University, Dubai P.O. Box 53044, United Arab Emirates
4
International Policing and Public Protection Research Institute (IPPPRI), Anglia Ruskin University, Cambridge CB1 1PT, UK
5
School of Computing, Mathematics and Data Science, Coventry University, Coventry CV1 5FB, UK
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(20), 9110; https://doi.org/10.3390/su16209110
Submission received: 17 May 2024 / Revised: 12 September 2024 / Accepted: 9 October 2024 / Published: 21 October 2024
(This article belongs to the Collection Operations Research: Optimization, Resilience and Sustainability)
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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.
Keywords: climate resilience; coastal flooding; Gaussian processes; probabilistic sensitivity analysis; wave overtopping; coastal defence climate resilience; coastal flooding; Gaussian processes; probabilistic sensitivity analysis; wave overtopping; coastal defence

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MDPI and ACS Style

Kent, P.; Abolfathi, S.; Al Ali, H.; Sedighi, T.; Chatrabgoun, O.; Daneshkhah, A. Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models. Sustainability 2024, 16, 9110. https://doi.org/10.3390/su16209110

AMA Style

Kent P, Abolfathi S, Al Ali H, Sedighi T, Chatrabgoun O, Daneshkhah A. Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models. Sustainability. 2024; 16(20):9110. https://doi.org/10.3390/su16209110

Chicago/Turabian Style

Kent, Paul, Soroush Abolfathi, Hannah Al Ali, Tabassom Sedighi, Omid Chatrabgoun, and Alireza Daneshkhah. 2024. "Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models" Sustainability 16, no. 20: 9110. https://doi.org/10.3390/su16209110

APA Style

Kent, P., Abolfathi, S., Al Ali, H., Sedighi, T., Chatrabgoun, O., & Daneshkhah, A. (2024). Resilient Coastal Protection Infrastructures: Probabilistic Sensitivity Analysis of Wave Overtopping Using Gaussian Process Surrogate Models. Sustainability, 16(20), 9110. https://doi.org/10.3390/su16209110

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