@inproceedings{9ffa5e78761d4a0698dcfb8117232a61,
title = "AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks",
abstract = "Data driven modeling based approaches have recently gained a lot of attention in many challenging meteorological applications including weather element forecasting. This paper introduces a novel data-driven predictive model based on TransUNet for precipitation nowcasting task. The TransUNet model which combines the Transformer and U-Net models has been previously successfully applied in medical segmentation tasks. Here, TransUNet is used as a core model and is further equipped with Convolutional Block Attention Modules (CBAM) and Depthwise-separable Convolution (DSC). The proposed Attention Augmented TransUNet (AA-TransUNet) model is evaluated on two distinct datasets: the Dutch precipitation map dataset and the French cloud cover dataset. The obtained results show that the proposed model outperforms other examined models on both tested datasets. Furthermore, the uncertainty analysis of the proposed AA-TransUNet is provided to give additional insights on its predictions.",
keywords = "UNet, Transformer, Precipitation Nowcasting, Cloud Cover Nowcasting, Deep Learning",
author = "Y.M. Yang and S. Mehrkanoon",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC) ; Conference date: 18-07-2022 Through 23-07-2022",
year = "2022",
doi = "10.1109/IJCNN55064.2022.9892376",
language = "English",
isbn = "9781728186719",
series = "IEEE International Joint Conference on Neural Networks Proceedings",
booktitle = "2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)",
publisher = "IEEE",
address = "United States",
}