用可解释AI分析极端气候下地表变化，预测精度达0.9055
Explainable Earth Surface Forecasting under Extreme Events
- 基于卷积LSTM架构，结合多源遥感数据建模
- 测试集R²达0.9055，能准确预测植被与反射率变化
- 揭示极端事件前兆信号，适合气候影响评估研究者
随着气候变化导致极端事件频发，高维地球观测数据为生态系统影响预测提供了新机遇，但其处理、可视化、建模与解释仍面临复杂挑战。本文基于全新构建的DeepExtremeCubes数据集，训练了一个卷积长短期记忆网络。该数据集包含约4万条全球范围的Sentinel-2 minicubes（2016年1月至2022年10月），涵盖极端气候事件发生地及其周边区域，包含标记的极端事件、气象数据、植被土地覆盖及地形图。模型通过核归一化差值植被指数预测未来反射率与植被影响，在测试集上达到0.9055的R²得分。利用可解释人工智能分析2020年10月南美中部复合热浪与干旱事件，选取一年前相同区域作为反事实对照，发现正常条件下平均气温与地表气压是主要预测因子；而在事件期间，蒸发与地表潜热通量的最小异常值成为主导因素。事件前属性贡献出现规律性转变，可能反映事件酝酿时长。论文代码已开源。
原文摘要 · Abstract (English)
With climate change-related extreme events on the rise, high dimensional Earth observation data presents a unique opportunity for forecasting and understanding impacts on ecosystems. This is, however, impeded by the complexity of processing, visualizing, modeling, and explaining this data. To showcase how this challenge can be met, here we train a convolutional long short-term memory-based architecture on the novel DeepExtremeCubes dataset. DeepExtremeCubes includes around 40,000 long-term Sentinel-2 minicubes (January 2016-October 2022) worldwide, along with labeled extreme events, meteorological data, vegetation land cover, and topography map, sampled from locations affected by extreme climate events and surrounding areas. When predicting future reflectances and vegetation impacts through kernel normalized difference vegetation index, the model achieved an R$^2$ score of 0.9055 in the test set. Explainable artificial intelligence was used to analyze the model's predictions during the October 2020 Central South America compound heatwave and drought event. We chose the same area exactly one year before the event as counterfactual, finding that the average temperature and surface pressure are generally the best predictors under normal conditions. In contrast, minimum anomalies of evaporation and surface latent heat flux take the lead during the event. A change of regime is also observed in the attributions before the event, which might help assess how long the event was brewing before happening. The code to replicate all experiments and figures in this paper is publicly available at https://github.com/DeepExtremes/txyXAI
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