Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model

Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades’ thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of subgrades, it is difficult for a single general neural net...

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Detalles Bibliográficos
Autores principales: Chen, Liyue, Liu, Xiao, Zeng, Chao, He, Xianzhi, Chen, Fengguang, Zhu, Baoshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370898/
https://www.ncbi.nlm.nih.gov/pubmed/35957299
http://dx.doi.org/10.3390/s22155742

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370898/
https://www.ncbi.nlm.nih.gov/pubmed/35957299
http://dx.doi.org/10.3390/s22155742

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