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

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,...

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Bibliographic Details
Published inSustainability Vol. 16; no. 20; p. 9110
Main Authors Kent, Paul, Abolfathi, Soroush, Al Ali, Hannah, Sedighi, Tabassom, Chatrabgoun, Omid, Daneshkhah, Alireza
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2024
Subjects
Analysis
Climatic changes
Coasts
Datasets
Defense
Floods
Fluid mechanics
Gaussian processes
Infrastructure
Infrastructure (Economics)
Machine learning
Performance evaluation
Regression analysis
Sea level
Shore protection
United Kingdom
United Kingdom
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su16209110