用于刻画印度降雨变率模式的图神经网络框架
A Graph Neural Network Framework for Characterizing Rainfall Variability Regimes across India
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中文总结 AI 辅助
该研究提出图神经网络框架，利用GSMaP ISRO数据对印度29026个网格点的降雨年际一致性分类，识别出降雨一致区域，发现高降雨量区时间可重复性更高的关联，准确率达96.8%。
中文摘要 AI 辅助
印度夏季风存在显著的空间变异。尽管先前研究主要聚焦于降雨量预报，但很少关注某一地点的季节性降雨轨迹年际间重复的一致性。我们提出一种基于图的机器学习框架，按这种年际一致性对印度各地进行分类。利用2001至2022年的GSMaP ISRO数据（不含2012年），我们为29026个网格点构建图，其中节点代表单个年份，边表示余弦相似度。图卷积网络以96.8%的准确率将地点分类为一致或不稳定。将该模型应用于印度陆地，成功识别出西高止山脉、印度东北部及印度中部部分区域为一致区域。此分类通过统计检验和时间稳定性分析得到严格验证，在两个独立时间区间间显示出93.6%的一致性。关键在于，结果揭示了此前未报道的关联：降雨量更高的区域也是年际间时间上最可重复的区域，呈现出一种新兴的空间连贯结构。
英文摘要
The Indian Summer Monsoon shows significant spatial variation. While prior work primarily focused on forecasting rainfall amounts, little attention has been given to how consistently a location's seasonal rainfall trajectory repeats from year to year. We introduce a graph-based machine learning framework to classify locations across India by this inter-annual consistency. Using 2001 to 2022 GSMaP ISRO data (excluding 2012), we constructed graphs for 29,026 grid points where nodes represent individual years and edges denote cosine similarity. A Graph Convolutional Network classified locations as either consistent or erratic with 96.8% accuracy. Applied to the Indian landmass, the model successfully identified the Western Ghats, Northeast India, and parts of central India as consistent regions. This classification was rigorously validated through statistical testing and temporal stability analysis, showing 93.6% agreement across two independent timeframes. Crucially, the results reveal a previously unreported coupling: regions with higher rainfall volumes are also the most temporally repeatable year-to-year, demonstrating an emergent, spatially coherent structure.
