AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 105 references that are in the map. First posted on arXiv 28 Jun 2024.
Builds on
18 of 105 references.
References · 18
- 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
- 2021Machine learning accelerated computational fluid dynamicscited by 461
- 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
- 2022Earthformer: Exploring Space-Time Transformers for Earth System Forecastingcited by 146
- 2023Machine learning with data assimilation and uncertainty quantification for dynamical systems: a reviewcited by 74
- 2019Analog forecasting of extreme-causing weather patterns using deep learningcited by 33
- 2023Discovering Causal Relations and Equations from Datacited by 33
- 2023chatClimate: Grounding Conversational AI in Climate Sciencecited by 32
- 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 25
- 2019Causal discovery in heavy-tailed modelscited by 18
- 2021Generating Physically-Consistent Satellite Imagery for Climate Visualizationscited by 13
- 2020Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciencescited by 9
6 more
- 2021Deep Learning-based Extreme Heatwave Forecastcited by 9
- 2022Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of datacited by 7
- 2020Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysiscited by 4
- 2021Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flowcited by 4
- 2022Identifying the Causes of Pyrocumulonimbus (PyroCb)cited by 4
- 2019Climate extreme event attribution using multivariate peaks-over-thresholds modeling and counterfactual theorycited by 1
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Citing papers per year: 2025: 3.
3 papers.