DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events


  • Tyler Wilson Michigan State University
  • Pang-Ning Tan Michigan State University
  • Lifeng Luo Michigan State University



Data Mining & Knowledge Management (DMKM), Machine Learning (ML)


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.




How to Cite

Wilson, T., Tan, P.-N., & Luo, L. (2022). DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4245-4253.



AAAI Technical Track on Data Mining and Knowledge Management