GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
CoRR(2024)
摘要
Accurate precipitation nowcasting is essential for various purposes,
including flood prediction, disaster management, optimizing agricultural
activities, managing transportation routes and renewable energy. While several
studies have addressed this challenging task from a sequence-to-sequence
perspective, most of them have focused on a single area without considering the
existing correlation between multiple disjoint regions. In this paper, we
formulate precipitation nowcasting as a spatiotemporal graph sequence
nowcasting problem. In particular, we introduce Graph Dual-stream Convolutional
Attention Fusion (GD-CAF), a novel approach designed to learn from historical
spatiotemporal graph of precipitation maps and nowcast future time step ahead
precipitation at different spatial locations. GD-CAF consists of
spatio-temporal convolutional attention as well as gated fusion modules which
are equipped with depthwise-separable convolutional operations. This
enhancement enables the model to directly process the high-dimensional
spatiotemporal graph of precipitation maps and exploits higher-order
correlations between the data dimensions. We evaluate our model on seven years
of precipitation maps across Europe and its neighboring areas collected from
the ERA5 dataset, provided by Copernicus. The model receives a fully connected
graph in which each node represents historical observations from a specific
region on the map. Consequently, each node contains a 3D tensor with time,
height, and width dimensions. Experimental results demonstrate that the
proposed GD-CAF model outperforms the other examined models. Furthermore, the
averaged seasonal spatial and temporal attention scores over the test set are
visualized to provide additional insights about the strongest connections
between different regions or time steps. These visualizations shed light on the
decision-making process of our model.
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