AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

TL;DR

This paper reviews how AI techniques are applied to analyze, predict, and understand extreme weather and environmental events, addressing challenges like data limitations and model transparency to improve disaster response.

Contribution

It provides a comprehensive overview of AI methodologies for extreme event modeling, highlighting challenges and proposing collaborative solutions for practical, trustworthy applications.

Findings

AI improves prediction of floods, droughts, wildfires, and heatwaves.

Challenges include limited data, real-time integration, and model interpretability.

Emphasizes collaboration for trustworthy AI solutions.

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.