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ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature

Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature datasets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels.

Ketersediaan
290551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd..,
Deskripsi Fisik
9 hlm PDF, 2.716 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.3, December 2022
Subjek
Graph neural network
GraphSAGE
ResNet
Global temperature prediction
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature
    Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the strong potential of deep neural network for extracting data features, this paper proposes a data-driven model, ResGraphNet, which improves the prediction accuracy of time series by an embedded residual module in GraphSAGE layers. The experimental results of a global mean temperature dataset, HadCRUT5, show that compared with 11 traditional prediction technologies, the proposed ResGraphNet obtains the best accuracy. The error indicator predicted by the proposed ResGraphNet is smaller than that of the other 11 prediction models. Furthermore, the performance on seven temperature datasets shows the excellent generalization of the ResGraphNet. Finally, based on our proposed ResGraphNet, the predicted 2022 annual anomaly of global temperature is 0.74722 °C, which provides confidence for limiting warming to 1.5 °C above pre-industrial levels.
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