| Summary: | Tropical cyclones (TCs) are one of the most destructive natural disasters, which can bring huge life and economic losses to the global coastal areas. Accurate TC intensity prediction is critical for disaster prevention and loss reduction, but the dynamic processes involved in TCs are complicated and not adequately understood, which make the intensity prediction is still a challenging task. In recent years, several deep-learning (DL)-based methods have been developed for TC prediction by mining TC intensity series or related environmental factors. However, information hidden between the two different data sources is generally ignored. Here, a novel DL-based TC intensity prediction network named Pre_3D is proposed, which aimed to mine of inter- and intra-patterns of TC intensity and related external factors independently by separate feature extraction sub-networks. An MLP network is adopted to achieve adaptive fusion of the two patterns for accurate TCs intensity prediction. TC records from several agencies were used to evaluate generalizability of the proposed framework and extensive experiments were conducted validate its effectiveness. The experimental results demonstrate that the models based on the Pre_3D framework achieved considerable performance. ConvGRU-based Pre_3D yields a significant improvement of over 15% in prediction accuracy in 24 h prediction relative to official agencies.
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