Physical Structure Representation and Environmental Data Fusion for Cyclone Intensity Prediction

Accurate tropical cyclone (TC) intensity prediction plays a crucial role in providing early warnings to coastal regions, helping prevent economic losses and ensuring the safety of individuals. Recently, deep learning (DL) for remote sensing images has been applied to TC intensity prediction and has achieved great success. However, most existing DL-based methods are unable to effectively leverage the changes in cyclone structure, which are important for TC intensity prediction. Moreover, these methods neglect the influence of physical environmental factors on TC intensity. In this paper, we propose a novel framework with Structural and Environmental Data Fusion Network (SEF-Net) to predict the maximum sustained wind (MSW) speed values near cyclone centers. Specifically, we extract spectral-spatial-temporal correlations by learning the changes in cyclone eye regions and cloud bands at different time steps. Subsequently, we perform multimodal fusion of image features and environmental factors to enhance the prediction accuracy of the network. Experimental results show that the proposed framework outperforms several state-of-the-art methods for TC intensity prediction on various cyclone datasets.