DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events
DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events
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DOI：
10.1609/aaai.v36i4.20344
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
T. Wilson;Pang-Ning Tan;L. Luo
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文献类型：
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作者：
T. Wilson;Pang-Ning Tan;L. Luo
地理时空数据在众多应用领域中无处不在，这些丰富的数据集可以用来预测极端事件，如疾病爆发、洪水、犯罪高峰等。然而，由于极端事件非常罕见，预测它们是一个困难的问题。基于极值理论的统计方法为极值分布的建模提供了一种系统的方法。特别地，广义帕累托分布（GPD）对于建模超过特定阈值的超额值的分布是有用的。然而，将这种方法应用于大规模的地理时空数据是一个挑战，因为在捕捉极端事件在多个位置之间的复杂的空间关系的困难。本文提出了一个深度学习框架，用于长期预测不同位置的极值分布。我们强调了它的计算挑战，并提出了一个新的框架，将卷积神经网络与深度集和GPD相结合。我们证明了我们的方法在模拟极端气候事件的真实数据集上的有效性。
Geospatio-temporal data are pervasive across numerous application domains.These rich datasets can be harnessed to predict extreme events such as disease outbreaks, flooding, crime spikes, etc. However, since the extreme events are rare, predicting them is a hard problem. Statistical methods based on extreme value theory provide a systematic way for modeling the distribution of extreme values. In particular, the generalized Pareto distribution (GPD) is useful for modeling the distribution of excess values above a certain threshold. However, applying such methods to large-scale geospatio-temporal data is a challenge due to the difficulty in capturing the complex spatial relationships between extreme events at multiple locations. This paper presents a deep learning framework for long-term prediction of the distribution of extreme values at different locations. We highlight its computational challenges and present a novel framework that combines convolutional neural networks with deep set and GPD. We demonstrate the effectiveness of our approach on a real-world dataset for modeling extreme climate events.