Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

Yongjiao Sun, Yaning Song, Baiyou Qiao, Boyang Li and Guanfeng Liu

Complexity, 2021, vol. 2021, 1-12

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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/complexity/2021/5661292.pdf (application/pdf)
http://downloads.hindawi.com/journals/complexity/2021/5661292.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5661292

DOI: 10.1155/2021/5661292

Access Statistics for this article

More articles in Complexity from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().