Predicting extreme events from data using deep machine learning: when and where
This work addresses predicting extreme events in nonlinear physical systems, such as weather patterns, but is incremental as it applies existing deep learning methods to this specific domain.
The authors tackled the problem of predicting extreme events in time and space from 2D data using a deep convolutional neural network framework, achieving validated predictions on synthetic and real-world wind speed data with trade-offs in horizon, resolution, and accuracy.
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 Ginzburg-Landau 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.