Research on short-term weather forecast model based on transformer combined with Seq2Seq model

YuHao Zhou; ShuaiLong Ren; Fei Luo

doi:10.1117/12.2668326

20 April 2023 Research on short-term weather forecast model based on transformer combined with Seq2Seq model

YuHao Zhou, ShuaiLong Ren, Fei Luo

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126020F (2023) https://doi.org/10.1117/12.2668326
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Weather forecast is very important for people's production and life. In recent years, with the rapid development of deep learning, the powerful feature expression ability of deep neural networks makes the effect of prediction obviously improved. In this paper, the Transformer combined with the Seq2Seq model to extract the spatial and temporal characteristics of data, and multiple meteorological elements in the future can be predicted from the historical data of multiple meteorological elements. Through comparative experiments, the effectiveness of the Transformer combined with the Seq2Seq model in multi-factor short-term weather forecast has been verified, and the accuracy of meteorological factors prediction has been improved.

Citation Download Citation

YuHao Zhou, ShuaiLong Ren, and Fei Luo "Research on short-term weather forecast model based on transformer combined with Seq2Seq model", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126020F (20 April 2023); https://doi.org/10.1117/12.2668326

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