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.

近年来，人工智能（AI）深刻影响了各个领域，包括地球系统科学。本文回顾了如何利用 AI 分析极端事件（如洪水、干旱、野火和热浪），强调创建准确、透明和可靠的 AI 模型的重要性。我们讨论了处理有限数据、实时信息整合、部署模型以及使其可理解的挑战，这些对于赢得利益相关者的信任和满足监管需求至关重要。我们概述了如何通过 AI 更有效地识别和解释极端事件，提高灾害应对和沟通能力。强调跨不同领域的合作，以创建实用、可理解和可信赖的 AI 解决方案，用于分析和预测极端事件。这种合作努力旨在增强灾害准备和减少灾害风险。

极端事件建模和理解的人工智能：方法和挑战

AI for Extreme Event Modeling and Understanding: Methodologies and  Challenges

As the world marked the midterm of the Sendai Framework for Disaster Risk
Reduction 2015-2030, many countries are still struggling to monitor their
climate and disaster risk because of the expensive large-scale survey of the
distribution of exposure and physical vulnerability and, hence, are not on
track in reducing risks amidst the intensifying effects of climate change. We
present an ongoing effort in mapping this vital information using machine
learning and time-series remote sensing from publicly available Sentinel-1 SAR
GRD and Sentinel-2 Harmonized MSI. We introduce the development of
"OpenSendaiBench" consisting of 47 countries wherein most are least developed
(LDCs), trained ResNet-50 deep learning models, and demonstrated the region of
Dhaka, Bangladesh by mapping the distribution of its informal constructions. As
a pioneering effort in auditing global disaster risk over time, this paper aims
to advance the area of large-scale risk quantification in informing our
collective long-term efforts in reducing climate and disaster risk.

以机器学习和遥感监测为基础，该研究致力于利用 Sentinel-1 SAR GRD 和 Sentinel-2 Harmonized MSI 等公开数据，对 47 个国家（大多数为最不发达国家）进行全球灾害风险评估，以推动减少全球气候和灾害风险的长期努力。