AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
Abstract
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.
Cite
@article{arxiv.2406.20080,
title = {AI for Extreme Event Modeling and Understanding: Methodologies and Challenges},
author = {Gustau Camps-Valls and Miguel-Ángel Fernández-Torres and Kai-Hendrik Cohrs and Adrian Höhl and Andrea Castelletti and Aytac Pacal and Claire Robin and Francesco Martinuzzi and Ioannis Papoutsis and Ioannis Prapas and Jorge Pérez-Aracil and Katja Weigel and Maria Gonzalez-Calabuig and Markus Reichstein and Martin Rabel and Matteo Giuliani and Miguel Mahecha and Oana-Iuliana Popescu and Oscar J. Pellicer-Valero and Said Ouala and Sancho Salcedo-Sanz and Sebastian Sippel and Spyros Kondylatos and Tamara Happé and Tristan Williams},
journal= {arXiv preprint arXiv:2406.20080},
year = {2024}
}