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.

中文翻译：

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基于复杂特征和多任务学习的分布式台风轨迹预测

台风是常见的自然现象，通常会带来灾难性后果，尤其是在沿海地区。因此，台风路径预测一直是一个重要的研究课题。它主要涉及根据台风的历史预测台风的运动。然而，台风的形成和运动是一个复杂的过程，这反过来又使准确预测变得更加复杂；台风的潜在位置与历史和未来因素有关。现有的作品并没有充分考虑这些因素；因此，提高预测准确性的空间很大。为此，我们提出了一种新颖的台风路径预测框架，包括复杂的历史特征——气候、地理和物理特征——以及基于多任务学习的深度学习网络。我们在分布式系统中实现了该框架，从而提高了网络的训练效率。我们在真实数据集上验证了所提出框架的效率。