Evaluation of Clustering Techniques for Revealing Dependence Between Wind Speed and Surge Height
Paulina E. Kindermann (TU Delft - Hydraulic Structures and Flood Risk, HKV Lijn in Water - Delft)
José A.A. Antolínez (TU Delft - Coastal Engineering)
Oswaldo Morales-Nápoles (TU Delft - Hydraulic Structures and Flood Risk)
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Abstract
Extreme storms over the North Sea drive coastal flood risk in the Netherlands, causing high waves and extreme sea levels. Designing flood defenses requires accurate statistical extrapolation of hydraulic load conditions with return periods of 1,000 years or more. This is a challenging task given limited observational data. This study uses a large, simulated dataset (~9,000 years) to explore the statistical dependence between extreme wind speed u and surge height s. Storms were clustered using several techniques. Self-organizing maps (SOM) effectively captured physical relationships, such as the influence of wind direction and tidal offset on storm dynamics, however variability in statistical dependence between u and s for different clusters was better represented using manual clusters. Copula models were fitted to the cluster data, with the BB8 copula outperforming others. This study illustrates the potential of machine learning to identify patterns in large datasets while emphasizing the relevance of manual clustering approaches for revealing nuanced statistical dependencies critical to flood risk assessment.
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