Predicting extreme events from data using deep machine learning: when and where

Junjie Jiang, Zi-Gang Huang, Celso Grebogi, Ying-Cheng Lai* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the 2D complex GinzburgLandau equation and empirical wind speed data of the North Atlantic ocean to demonstrate and validate our
machine-learning based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme event on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.
Original languageEnglish
Article number023028
Number of pages14
JournalPhysical Review Research
Issue number2
Early online date11 Apr 2022
Publication statusPublished - 1 Jun 2022

Bibliographical note

The work at Arizona State University was supported by AFOSR under Grant No. FA9550-21-1-0438 and by ONR under Grant No. N00014-21-1-2323. The work at Xi’an Jiaotong University was supported by the National Key R&D Program of China (Grant No. 2021ZD0201300), National Natural Science Foundation of China (Grant No. 11975178), and K. C. Wong Education Foundation.


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