GD-CAF: Graph Dual-stream Convolutional Attention Fusion for
Precipitation Nowcasting
- URL: http://arxiv.org/abs/2401.07958v2
- Date: Mon, 26 Feb 2024 16:21:55 GMT
- Title: GD-CAF: Graph Dual-stream Convolutional Attention Fusion for
Precipitation Nowcasting
- Authors: Lorand Vatamany, Siamak Mehrkanoon
- Abstract summary: We introduce Graph Dual-streamtemporal Conal Attention Fusion (GD-CAF) to learn from historical graph of precipitation maps and nowcast future time step ahead.
GD-CAF consists of gated-temporal convolutional attention as well as fusion modules equipped with depthwise-separable convolutional operations.
We evaluate our model seven years of precipitation maps across Europe and its neighboring areas collected from the ERA5 dataset.
- Score: 1.642094639107215
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Accurate precipitation nowcasting is essential for various applications,
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 Climate Change Services. The
experimental results reveal the superior performance of the GD-CAF model
compared to the other examined models. Additionally, visualizations of averaged
seasonal spatial and temporal attention scores across the test set offer
valuable insights into the most robust connections between diverse regions or
time steps.
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