RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
This work addresses the problem of predicting heavy rainfall for disaster prevention, but it is incremental as it builds on existing ConvRNN methods with attention mechanisms.
The paper tackles precipitation nowcasting by proposing RAP-Net, which incorporates Region Attention Block and Recall Attention Mechanism into ConvRNN to improve forecasting accuracy, especially in heavy and middle rainfall regions, and reports outperforming state-of-the-art methods.
Natural disasters caused by heavy rainfall often cost huge loss of life and property. To avoid it, the task of precipitation nowcasting is imminent. To solve the problem, increasingly deep learning methods are proposed to forecast future radar echo images and then the predicted maps have converted the distribution of rainfall. The prevailing spatiotemporal sequence prediction methods apply ConvRNN structure which combines the Convolution and Recurrent neural network. Although improvements based on ConvRNN achieve remarkable success, these methods ignore capturing both local and global spatial features simultaneously, which degrades the nowcasting in the region of heavy rainfall. To address this issue, we proposed the Region Attention Block (RAB) and embed it into ConvRNN to enhance the forecast in the area with strong rainfall. Besides, the ConvRNN models are hard to memory longer history representations with limited parameters. Considering it, we propose Recall Attention Mechanism (RAM) to improve the prediction. By preserving longer temporal information, RAM contributes to the forecasting, especially in the middle rainfall intensity. The experiments show that the proposed model Region Attention Predictive Network (RAP-Net) has outperformed the state-of-art method.