Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

Abstract

Typhoons are common natural phenomena that often have disastrous aftermaths, particularly in coastal areas. Consequently, typhoon track prediction has always been an important research topic. It chiefly involves predicting the movement of a typhoon according to its history. However, the formation and movement of typhoons is a complex process, which in turn makes accurate prediction more complicated; the potential location of typhoons is related to both historical and future factors. Existing works do not fully consider these factors; thus, there is significant room for improving the accuracy of predictions. To this end, we presented a novel typhoon track prediction framework comprising complex historical features—climatic, geographical, and physical features—as well as a deep-learning network based on multitask learning. We implemented the framework in a distributed system, thereby improving the training efficiency of the network. We verified the efficiency of the proposed framework on real datasets.

Typhoon Track Prediction Based on Deep Learning

“…The literature [22] proposed a fused deep learning method for typhoon path prediction based on reanalysis information, which fuses past track data and reanalysis atmospheric images to build a neural network model with lower time cost and better real-time performance compared to other existing dynamical prediction models. Sun et al [23] proposed a deep learning model in a distributed system, based on complex features and learned by task, for implementing a typhoon path prediction model. Mario et al [24] used Generative Adversarial Networks (GAN) for typhoon path prediction using satellite cloud images as input, which provides a visual complement to the typhoon path prediction method, but the prediction model constructed by this method converges slowly.…”