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.

翻译：本文提出了一种新颖的带有空间正则化的图注意力自编码器（GAE），以应对1990年至2015年印度时空降雨数据中可扩展异常检测的挑战。我们的模型利用图注意力网络（GAT）捕捉数据中的空间依赖性和时间动态，并通过空间正则化项进一步增强，以确保地理连贯性。我们分别使用印度气象部门的降雨、气压和温度属性以及ERA5单层再分析数据构建了两个图数据集。我们的网络在数据的图表示上运行，其中节点代表地理位置，边通过事件同步推断，表示降雨事件的显著共现。通过大量实验，我们证明我们的GAE能够有效识别印度全境的异常降雨模式。我们的工作为气候科学中复杂的时空异常检测方法铺平了道路，有助于制定更好的气候变化准备和应对策略。