用空间正则化图注意力模型检测印度1990-2015年降雨异常
Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes
- 构建基于事件同步的图结构，节点为地理位置，边表示强降雨共现
- 在多源数据上验证，能有效识别全境降雨异常模式
- 适合气候异常监测与防灾响应研究者使用
我们提出一种带空间正则化的图注意力自编码器（GAE），用于解决印度1990至2015年间大规模时空降雨数据中的异常检测难题。模型利用图注意力网络（GAT）捕捉空间依赖与时间动态，通过空间正则化项保证地理一致性。基于印度气象局和ERA5再分析单层数据，构建了两个含降雨、气压与温度属性的图数据集。网络以地理点为节点，通过事件同步推断边，表示显著的降雨事件共现。大量实验表明，该GAE能有效识别印度全域的异常降雨模式。本工作为气候科学中的复杂时空异常检测提供了新方法，有助于提升气候变化应对能力。
原文摘要 · Abstract (English)
We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015. Our model leverages a Graph Attention Network (GAT) to capture spatial dependencies and temporal dynamics in the data, further enhanced by a spatial regularization term ensuring geographic coherence. We construct two graph datasets employing rainfall, pressure, and temperature attributes from the Indian Meteorological Department and ERA5 Reanalysis on Single Levels, respectively. Our network operates on graph representations of the data, where nodes represent geographic locations, and edges, inferred through event synchronization, denote significant co-occurrences of rainfall events. Through extensive experiments, we demonstrate that our GAE effectively identifies anomalous rainfall patterns across the Indian landscape. Our work paves the way for sophisticated spatiotemporal anomaly detection methodologies in climate science, contributing to better climate change preparedness and response strategies.
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