Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes
This work addresses anomaly detection in climate science for better climate change preparedness, but it appears incremental as it builds on existing graph-based methods with spatial regularization.
The authors tackled scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015 by introducing a Graph Attention Autoencoder with spatial regularization, which effectively identified anomalous rainfall patterns.
We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015. Our model leverages a Graph Attention Network (GAT) to capture spatial dependencies and temporal dynamics in the data, further enhanced by a spatial regularization term ensuring geographic coherence. We construct two graph datasets employing rainfall, pressure, and temperature attributes from the Indian Meteorological Department and ERA5 Reanalysis on Single Levels, respectively. Our network operates on graph representations of the data, where nodes represent geographic locations, and edges, inferred through event synchronization, denote significant co-occurrences of rainfall events. Through extensive experiments, we demonstrate that our GAE effectively identifies anomalous rainfall patterns across the Indian landscape. Our work paves the way for sophisticated spatiotemporal anomaly detection methodologies in climate science, contributing to better climate change preparedness and response strategies.