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A dynamic extreme value model with applications to volcanic eruption forecasting

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Title: A dynamic extreme value model with applications to volcanic eruption forecasting
Authors: Nguyen, M
Veraart, A
Taisne, B
Ting, TC
Lallemant, D
Item Type: Journal Article
Abstract: Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundations to accurately characterise extremes. This motivates the development of extreme value models for extreme event forecasting. In this paper, a dynamic extreme value model is proposed for forecasting volcano eruptions. This is inspired by one recently introduced for financial risk forecasting with high-frequency data. Using a case study of the Piton de la Fournaise volcano, it is shown that the modelling framework is widely applicable, flexible and holds strong promise for natural hazard forecasting. The value of using EVT-informed thresholds to identify and model extreme events is shown through forecast performance, and considerations to account for the range of observed events are discussed.
Issue Date: 1-May-2024
Date of Acceptance: 18-Sep-2023
URI: http://hdl.handle.net/10044/1/106940
DOI: 10.1007/s11004-023-10109-2
ISSN: 1573-8868
Publisher: Springer
Start Page: 841
End Page: 865
Journal / Book Title: Mathematical Geosciences
Volume: 56
Copyright Statement: © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publication Status: Published
Online Publication Date: 2023-10-30
Appears in Collections:Statistics
Faculty of Natural Sciences
Mathematics