Identifying Spatio-Temporal Drivers of Extreme Events
Keywords: anomaly detection, weakly supervised learning, Earth science, climate science, remote sensing, deep learning
TL;DR: We present a deep learning model, designed to leverage climate data to identify the drivers of extreme events impacts.
Abstract: The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data. The task, however, is very challenging since there are time delays between extremes and their drivers, and the spatial response of such drivers is inhomogeneous. In this work, we propose a first approach and benchmarks to tackle this challenge. Our approach is trained end-to-end to predict spatio-temporally extremes and spatio-temporally drivers in the physical input variables jointly. By enforcing the network to predict extremes from spatio-temporal binary masks of identified drivers, the network successfully identifies drivers that are correlated with extremes. We evaluate our approach on three newly created synthetic benchmarks, where two of them are based on remote sensing or reanalysis climate data, and on two real-world reanalysis datasets. The source code and datasets are publicly available at the project page https://hakamshams.github.io/IDE.
Primary Area: Machine learning for physical sciences (for example: climate, physics)
Submission Number: 11216
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