# Multi-site modelling and reconstruction of past extreme skew surges along the French Atlantic coast

arXiv: https://arxiv.org/abs/2505.00835 · HTML: https://mlsanity.com/paper/2505.00835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 86 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 15 of 15)

- [A review of predictive uncertainty estimation with machine learning](https://mlsanity.com/paper/2209.08307/) (2022)
- [How to Evaluate Uncertainty Estimates in Machine Learning for Regression?](https://mlsanity.com/paper/2106.03395/) (2021)
- [Decompositions of Dependence for High-Dimensional Extremes](https://mlsanity.com/paper/1612.07190/) (2016)
- [k-means clustering of extremes](https://mlsanity.com/paper/1904.02970/) (2019)
- [Principal Component Analysis for Multivariate Extremes](https://mlsanity.com/paper/1906.11043/) (2019)
- [Sparsity in Multivariate Extremes with Applications to Anomaly Detection](https://mlsanity.com/paper/1507.05899/) (2015)
- [Sparse regular variation](https://mlsanity.com/paper/1907.00686/) (2019)
- [Determining the Dependence Structure of Multivariate Extremes](https://mlsanity.com/paper/1809.01606/) (2018)
- [On Regression in Extreme Regions](https://mlsanity.com/paper/2303.03084/) (2023)
- [Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking](https://mlsanity.com/paper/1603.09584/) (2016)
- [Identifying groups of variables with the potential of being large simultaneously](https://mlsanity.com/paper/1802.09977/) (2018)
- [Multivariate peaks over thresholds models](https://mlsanity.com/paper/1603.06619/) (2016)
- [Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes](https://mlsanity.com/paper/2308.01023/) (2023)
- [Distributional regression models for Extended Generalized Pareto distributions](https://mlsanity.com/paper/2209.04660/) (2022)
- [Accounting for Seasonality in Extreme Sea Level Estimation](https://mlsanity.com/paper/2207.09870/) (2022)

## Built on by (most cited first, 1 of 1)

- [Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach](https://mlsanity.com/paper/2606.00265/) (2026)

## Abstract (arXiv)

Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew surges along the French Atlantic coast, with a particular emphasis on investigating the extremal dependence structure between stations. We employ the peak-over-threshold framework, where a multivariate extreme event is defined whenever at least one location records a large value, though not necessarily all stations simultaneously. A novel method for determining an appropriate level (threshold) above which observations can be classified as extreme is proposed. Two complementary approaches are explored. First, the multivariate generalized Pareto distribution is employed to model extremes, leveraging its properties to derive a generative model that predicts extreme skew surges at one station based on observed extremes at nearby stations. Second, a novel extreme regression framework is assessed for point predictions. This specific regression framework enables accurate point predictions using only the 'angle' of input variables, i.e., input variables divided by their norms. The ultimate objective is to reconstruct historical skew surge time series at stations with limited data. This is achieved by integrating extreme skew surge data from stations with longer records, such as Brest and Saint-Nazaire, which provide over 150 years of observations.