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 in:Sustainability 2024-10, Vol.16 (20), p.9110
Main Authors: Kent, Paul, Abolfathi, Soroush, Al Ali, Hannah, Sedighi, Tabassom, Chatrabgoun, Omid, Daneshkhah, Alireza
Format: Article
Language:English
Subjects:
Coastal ecosystems
Coasts
Datasets
Defense
Environmental aspects
Evaluation
Floods
Fluid mechanics
Infrastructure
Infrastructure (Economics)
Machine learning
Management
Performance evaluation
Protection and preservation
Regression analysis
Sea level
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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