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• The SAF-Net is proposed to combine domain-knowledge and data-driven knowledge. • The spatial attention is used to automatically selected high-response wind speed area. • Extensive experiments is proceeded in the real-world dataset. A typhoon is a destructive weather system that can cause severe casualties and economic losses. Typhoon intensity (TI) is a measurement to evaluate its ruinous degree. Hence, typhoon intensity prediction is an important research problem and many methods have been proposed. However, most of the existing approaches have very limited capability to combine the 2D Typhoon Structure Domain-expert Knowledge (2D-TSDK) and the 3D Typhoon Structure Data-driven Knowledge (3D-TSDK) for the TI prediction. To address this issue, this paper proposes a spatio-temporal deep learning method named Spatial Attention Fusing Network (SAF-Net). The designed model aims to fuse the 2D-TSDK and the 3D-TSDK by developing a specific Wide & Deep framework. In the data-driven component, a special Spatial Attention (SA) module is designed to automatically select high-response wind speed areas and embedded into a three-branch CNN to exploit the 3D-TSDK. Then, the Wide & Deep framework integrates the 2D-TSDK and the 3D-TSDK for the TI prediction. Comprehensive experiments have been conducted on a real-world dataset, and the result shows that the proposed method outperforms state-of-the-art typhoon intensity prediction methods. The code is available in GitHub: https://github.com/xuguangning1218/TI_Prediction [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s00521-021-06724-x. B Correction to: Neural Computing and Applications (2022) 34:5905-5921 b https://doi.org/10.1007/s00521-021-06724-x The Eq. (10) in the article is corrected as below: 10 HT ht Graph Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]