AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

TL;DR

The paper addresses AI for extreme event modeling and understanding, focusing on detection, forecasting, attribution, explanation, and risk communication amid data scarcity and nonstationarity. It surveys AI methods across modeling, trustworthiness, and operationalization, outlining challenges and opportunities. Case studies on droughts, heatwaves, wildfires, and floods illustrate how multimodal data, XAI, and causal inference improve detection, prediction, attribution, and communication. The authors advocate for interdisciplinary collaboration, robust uncertainty quantification, and real-time data integration to translate AI insights into actionable disaster readiness and risk reduction.

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.

Figures (3)

• Figure 1: A general AI-driven extreme event analysis pipeline. Different components in modeling and understanding extreme events using AI methodologies are interconnected, highlighting the flow from data collection and analysis to actionable insights/outputs and the challenges encountered. Note the feedback loops where AI does not only produce some relevant outputs and products from data (predictions, patterns and trends, climate attributions, and causal relations) but also may help suggest areas for improvement and adaptation in methodologies to overcome identified challenges, redefine the hypotheses and challenges themselves, as well as inform data collection and preprocessing.
• Figure 2: Components in an AI pipeline for extreme events. AI mainly exploits spatio-temporal Earth observation, reanalysis, and climate data to answer "what'-questions (top row): detection of events, prediction, and impact assessment. AI can also be used for understanding events and thus answer "what if," "why," and "how sure" questions (middle row) and makes use of explainable AI (XAI) to identify relevant drivers of events, causality to understanding the system, estimate causal effects and impacts, and imagine counterfactual scenarios for attribution and uncertainty estimation to quantify trust and robustness for decision-making. Communicating extreme events and their impacts can benefit from statistical/machine learning (bottom row) by improving operationalization, ensuring fair and equitable narratives, and integrating language models in situation rooms for enhanced decision-making.
• Figure 3: Summary of case studies using AI to manage extreme events. Four use cases (drought, heatwaves, wildfires, and floods) are showcased where AI enables detection, forecasting, impact assessment, explanation, understanding, and communication of risk, providing a comprehensive solution for disaster management. (a) Droughts. Top: AI leverages multimodal data to predict Earth's surface dynamics, enhancing forecasts for crop yields, forest health, and drought impacts. Bottom: XAI techniques, like "neuron integrated gradients," elucidate the key factors driving severe drought conditions, highlighting variable interactions over time. (b) Heatwaves. Top: Variables of interest are extracted from heterogeneous data sources (images, time series, text) and potentially aggregated over space and/or time. Bottom Left: Relevant features can be extracted from the data using e.g., clustering techniques. Right: Prediction of heatwaves can be done in combination with dimensionality reduction tools or directly from the selected features. (c) Wildfires. Top: AI enhances understanding and prediction of wildfire dynamics, particularly for mega-fires intensified by global warming, by analyzing extensive datasets and differentiating fire types with XAI. Bottom: AI combined with causal inference aims to better detect and understand pyrocumulonimbus clouds, intense storm systems generated by large wildfires that complicate fire behavior prediction. (d) Floods. AI transforms flood risk communication by using realistic 3D visualizations and animations to depict rising water levels' impact on communities and infrastructure, making the information more relatable ( thisclimatedoesnotexist.com). AI-driven platforms analyze vast amounts of data from weather forecasts, river levels, and historical flood patterns to predict future events accurately, integrating this information with digital maps and urban models to identify high-risk areas ( climate-viz.github.io.com). This approach enhances flood risk management by allowing for targeted, personalized communication, enabling residents to receive specific alerts and visualize potential impacts on their homes. AI also supports the generation of detailed flood reports from various sources, enhancing preparedness and mitigation efforts ( floodbrain.com).