# Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting

arXiv: https://arxiv.org/abs/2401.07958 · HTML: https://mlsanity.com/paper/2401.07958/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [PreDiff: Precipitation Nowcasting with Latent Diffusion Models](https://mlsanity.com/paper/2307.10422/) (2023)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [Machine Learning for Precipitation Nowcasting from Radar Images](https://mlsanity.com/paper/1912.12132/) (2019)
- [Broad-UNet: Multi-scale feature learning for nowcasting tasks](https://mlsanity.com/paper/2102.06442/) (2021)
- [Spatio-temporal graph neural networks for multi-site PV power forecasting](https://mlsanity.com/paper/2107.13875/) (2021)
- [AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks](https://mlsanity.com/paper/2202.04996/) (2022)
- [Deep Graph Convolutional Networks for Wind Speed Prediction](https://mlsanity.com/paper/2101.10041/) (2021)
- [Precipitation Nowcasting with Satellite Imagery](https://mlsanity.com/paper/1905.09932/) (2019)
- [Wind speed prediction using multidimensional convolutional neural networks](https://mlsanity.com/paper/2007.12567/) (2020)
- [Deep coastal sea elements forecasting using U-Net based models](https://mlsanity.com/paper/2011.03303/) (2020)
- [Multistream Graph Attention Networks for Wind Speed Forecasting](https://mlsanity.com/paper/2108.07063/) (2021)
- [Prediction of severe thunderstorm events with ensemble deep learning and radar data](https://mlsanity.com/paper/2109.09791/) (2021)

## Built on by (most cited first, 7 of 7)

- [SSA-UNet: Advanced Precipitation Nowcasting via Channel Shuffling](https://mlsanity.com/paper/2504.18309/) (2025)
- [Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting](https://mlsanity.com/paper/2412.15917/) (2024)
- [Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNet](https://mlsanity.com/paper/2502.16116/) (2025)
- [Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting](https://mlsanity.com/paper/2507.19513/) (2025)
- [Understanding Multi-Agent Reasoning with Large Language Models for Cartoon VQA](https://mlsanity.com/paper/2601.03073/) (2026)
- [SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting](https://mlsanity.com/paper/2603.21879/) (2026)
- [A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions](https://mlsanity.com/paper/2604.10328/) (2026)

## Abstract (arXiv)

Accurate precipitation nowcasting is crucial for applications such as flood prediction, disaster management, agriculture optimization, and transportation management. While many studies have approached this task using sequence-to-sequence models, most focus on single regions, ignoring correlations between disjoint areas. We reformulate precipitation nowcasting as a spatiotemporal graph sequence problem. Specifically, we propose Graph Dual-stream Convolutional Attention Fusion, a novel extension of the graph attention network. Our model's dual-stream design employs distinct attention mechanisms for spatial and temporal interactions, capturing their unique dynamics. A gated fusion module integrates both streams, leveraging spatial and temporal information for improved predictive accuracy. Additionally, our framework enhances graph attention by directly processing three-dimensional tensors within graph nodes, removing the need for reshaping. This capability enables handling complex, high-dimensional data and exploiting higher-order correlations between data dimensions. Depthwise-separable convolutions are also incorporated to refine local feature extraction and efficiently manage high-dimensional inputs. We evaluate our model using seven years of precipitation data from Copernicus Climate Change Services, covering Europe and neighboring regions. Experimental results demonstrate superior performance of our approach compared to other models. Moreover, visualizations of seasonal spatial and temporal attention scores provide insights into the most significant connections between regions and time steps.