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

Chen, Liyue; Liu, Xiao; Zeng, Chao; He, Xianzhi; Chen, Fengguang; Zhu, Baoshan

Chen, Liyue; Liu, Xiao; Zeng, Chao; He, Xianzhi; Chen, Fengguang; Zhu, Baoshan.

Afiliação

Chen L; Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China.
Liu X; China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China.
Zeng C; Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China.
He X; China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China.
Chen F; China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China.
Zhu B; China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China.

Sensors (Basel) ; 22(15)2022 Aug 01.

Article em En | MEDLINE | ID: mdl-35957299

Assuntos

Redes Neurais de Computação; Previsões; Estações do Ano; Temperatura

Palavras-chave

complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); long short-term memory (LSTM); seasonal frozen subgrade; temperature prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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